%%% -*-BibTeX-*- %%% ==================================================================== %%% BibTeX-file{ %%% author = "Nelson H. F. Beebe", %%% version = "1.04", %%% date = "20 August 2024", %%% time = "09:41:26 MDT", %%% filename = "telo.bib", %%% address = "University of Utah %%% Department of Mathematics, 110 LCB %%% 155 S 1400 E RM 233 %%% Salt Lake City, UT 84112-0090 %%% USA", %%% telephone = "+1 801 581 5254", %%% FAX = "+1 801 581 4148", %%% URL = "http://www.math.utah.edu/~beebe", %%% checksum = "63515 2085 8906 88753", %%% email = "beebe at math.utah.edu, beebe at acm.org, %%% beebe at computer.org (Internet)", %%% codetable = "ISO/ASCII", %%% keywords = "ACM Transactions on Evolutionary Learning and %%% Optimization (TELO); bibliography; BibTeX", %%% license = "public domain", %%% supported = "yes", %%% docstring = "This is a COMPLETE BibTeX bibliography for %%% ACM Transactions on Evolutionary Learning and %%% Optimization (TELO) (CODEN ????, ISSN %%% 2688-299X (print), 2688-3007 (electronic)). %%% The journal appears quarterly, and %%% publication began with volume 1, number 1, in %%% June 2021. %%% %%% At version 1.04, the COMPLETE journal %%% coverage looked like this: %%% %%% 2021 ( 17) 2023 ( 16) %%% 2022 ( 14) 2024 ( 18) %%% %%% Article: 65 %%% %%% Total entries: 65 %%% %%% The journal Web page can be found at: %%% %%% https://dl.acm.org/journal/telo %%% https://telo.acm.org/ %%% %%% The journal table of contents page is at: %%% %%% https://dl.acm.org/loi/telo %%% https://dlnext.acm.org/journal/telo %%% %%% Qualified subscribers can retrieve the full %%% text of recent articles in PDF form. %%% %%% The initial draft was extracted from the ACM %%% Web pages. %%% %%% ACM copyrights explicitly permit abstracting %%% with credit, so article abstracts, keywords, %%% and subject classifications have been %%% included in this bibliography wherever %%% available. Article reviews have been %%% omitted, until their copyright status has %%% been clarified. %%% %%% URL keys in the bibliography point to %%% World Wide Web locations of additional %%% information about the entry. %%% %%% BibTeX citation tags are uniformly chosen %%% as name:year:abbrev, where name is the %%% family name of the first author or editor, %%% year is a 4-digit number, and abbrev is a %%% 3-letter condensation of important title %%% words. Citation tags were automatically %%% generated by software developed for the %%% BibNet Project. %%% %%% In this bibliography, entries are sorted in %%% publication order, using ``bibsort -byvolume.'' %%% %%% The checksum field above contains a CRC-16 %%% checksum as the first value, followed by the %%% equivalent of the standard UNIX wc (word %%% count) utility output of lines, words, and %%% characters. This is produced by Robert %%% Solovay's checksum utility.", %%% } %%% ==================================================================== @Preamble{"\input bibnames.sty" # "\ifx \undefined \booktitle \def \booktitle #1{{{\em #1}}} \fi" # "\ifx \undefined \TM \def \TM {${}^{\sc TM}$} \fi" } %%% ==================================================================== %%% Acknowledgement abbreviations: @String{ack-nhfb = "Nelson H. F. Beebe, University of Utah, Department of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1 801 581 4148, e-mail: \path|beebe@math.utah.edu|, \path|beebe@acm.org|, \path|beebe@computer.org| (Internet), URL: \path|http://www.math.utah.edu/~beebe/|"} %%% ==================================================================== %%% Journal abbreviations: @String{j-TELO = "ACM Transactions on Evolutionary Learning and Optimization (TELO)"} %%% ==================================================================== %%% Bibliography entries: @Article{DeAth:2021:GGE, author = "George {De Ath} and Richard M. Everson and Alma A. M. Rahat and Jonathan E. Fieldsend", title = "Greed is Good: Exploration and Exploitation Trade-offs in {Bayesian} Optimisation", journal = j-TELO, volume = "1", number = "1", pages = "1:1--1:22", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3425501", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:09 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3425501", abstract = "The performance of acquisition functions for Bayesian optimisation to locate the global optimum of continuous functions is investigated in terms of the Pareto front between exploration and exploitation. We show that Expected Improvement (EI) and the Upper \ldots{}", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Branke:2021:ATE, author = "Juergen Branke and Darrell Whitley", title = "{{\booktitle{ACM Transactions on Evolutionary Learning and Optimization}}} Inaugural Issue Editorial", journal = j-TELO, volume = "1", number = "1", pages = "1e:1--1e:2", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3449277", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:09 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3449277", acknowledgement = ack-nhfb, articleno = "1e", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Corus:2021:SSE, author = "Dogan Corus and Andrei Lissovoi and Pietro S. Oliveto and Carsten Witt", title = "On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best", journal = j-TELO, volume = "1", number = "1", pages = "2:1--2:38", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3427474", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:09 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3427474", abstract = "We analyse the impact of the selective pressure for the global optimisation capabilities of steady-state evolutionary algorithms (EAs). For the standard bimodal benchmark function TwoMax, we rigorously prove that using uniform parent selection leads to \ldots{}", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Jiang:2021:FCM, author = "Hao Jiang and Yuhang Wang and Ye Tian and Xingyi Zhang and Jianhua Xiao", title = "Feature Construction for Meta-heuristic Algorithm Recommendation of Capacitated Vehicle Routing Problems", journal = j-TELO, volume = "1", number = "1", pages = "3:1--3:28", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3447540", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:09 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3447540", abstract = "The algorithm recommendation is attracting increasing attention in solving real-world capacitated vehicle routing problems (CVRPs), due to the fact that existing meta-heuristic algorithms often show different performances on different CVRPs. To \ldots{}", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Lorandi:2021:GIR, author = "Michela Lorandi and Leonardo Lucio Custode and Giovanni Iacca", title = "Genetic Improvement of Routing Protocols for Delay Tolerant Networks", journal = j-TELO, volume = "1", number = "1", pages = "04:1--04:37", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3453683", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:09 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3453683", abstract = "Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of, e.g., (possibly, unmanned) vehicles and humans where, despite a lack of \ldots{}", acknowledgement = ack-nhfb, articleno = "04", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Dushatskiy:2021:NAD, author = "Arkadiy Dushatskiy and Tanja Alderliesten and Peter A. N. Bosman", title = "A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of {Walsh} Coefficients and Dependencies", journal = j-TELO, volume = "1", number = "2", pages = "5:1--5:23", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3453141", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:10 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3453141", abstract = "Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the \ldots{}", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Hemberg:2021:SCG, author = "Erik Hemberg and Jamal Toutouh and Abdullah Al-Dujaili and Tom Schmiedlechner and Una-May O'Reilly", title = "Spatial Coevolution for Generative Adversarial Network Training", journal = j-TELO, volume = "1", number = "2", pages = "6:1--6:28", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3458845", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:10 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3458845", abstract = "Generative Adversarial Networks (GANs) are difficult to train because of pathologies such as mode and discriminator collapse. Similar pathologies have been studied and addressed in competitive evolutionary computation by increased diversity. We study a \ldots{}", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Langdon:2021:GID, author = "William B. Langdon and Oliver Krauss", title = "Genetic Improvement of Data for Maths Functions", journal = j-TELO, volume = "1", number = "2", pages = "7:1--7:30", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3461016", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:10 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/elefunt.bib; http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3461016", abstract = "We use continuous optimisation and manual code changes to evolve up to 1024 Newton--Raphson numerical values embedded in an open source GNU C library glibc square root sqrt to implement a double precision cube root routine cbrt, binary logarithm log2 and reciprocal square root function for C in seconds. The GI inverted square root $ x{-1 / 2} $ is far more accurate than Quake's InvSqrt, Quare root. GI shows potential for automatically creating mobile or low resource mote smart dust bespoke custom mathematical libraries with new functionality.", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Nader:2021:EAF, author = "Andrew Nader and Danielle Azar", title = "Evolution of Activation Functions: an Empirical Investigation", journal = j-TELO, volume = "1", number = "2", pages = "8:1--8:36", month = jun, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3464384", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:10 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3464384", abstract = "The hyper-parameters of a neural network are traditionally designed through a time-consuming process of trial and error that requires substantial expert knowledge. Neural Architecture Search algorithms aim to take the human out of the loop by \ldots{}", acknowledgement = ack-nhfb, articleno = "8", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Li:2021:OAR, author = "Miqing Li", title = "Is Our Archiving Reliable? {Multiobjective} Archiving Methods on ``Simple'' Artificial Input Sequences", journal = j-TELO, volume = "1", number = "3", pages = "9:1--9:19", month = sep, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3465335", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:10 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3465335", abstract = "In evolutionary multiobjective optimisation (EMO), archiving is a common component that maintains an (external or internal) set during the search process, typically with a fixed size, in order to provide a good representation of high-quality solutions \ldots{}", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Liu:2021:CLC, author = "Yi Liu and Will N. Browne and Bing Xue", title = "A Comparison of Learning Classifier Systems' Rule Compaction Algorithms for Knowledge Visualization", journal = j-TELO, volume = "1", number = "3", pages = "10:1--10:38", month = sep, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3468166", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:10 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3468166", abstract = "Learning Classifier Systems (LCSs) are a paradigm of rule-based evolutionary computation (EC). LCSs excel in data-mining tasks regarding helping humans to understand the explored problem, often through visualizing the discovered patterns linking features \ldots{}", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Kelly:2021:ETP, author = "Stephen Kelly and Robert J. Smith and Malcolm I. Heywood and Wolfgang Banzhaf", title = "Emergent Tangled Program Graphs in Partially Observable Recursive Forecasting and {ViZDoom} Navigation Tasks", journal = j-TELO, volume = "1", number = "3", pages = "11:1--11:41", month = sep, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3468857", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:10 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3468857", abstract = "Modularity represents a recurring theme in the attempt to scale evolution to the design of complex systems. However, modularity rarely forms the central theme of an artificial approach to evolution. In this work, we report on progress with the recently \ldots{}", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Xu:2021:COC, author = "Peilan Xu and Wenjian Luo and Xin Lin and Jiajia Zhang and Yingying Qiao and Xuan Wang", title = "Constraint-Objective Cooperative Coevolution for Large-scale Constrained Optimization", journal = j-TELO, volume = "1", number = "3", pages = "12:1--12:26", month = sep, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3469036", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Sat Aug 21 15:11:10 MDT 2021", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3469036", abstract = "Large-scale optimization problems and constrained optimization problems have attracted considerable attention in the swarm and evolutionary intelligence communities and exemplify two common features of real problems, i.e., a large scale and constraint \ldots{}", acknowledgement = ack-nhfb, articleno = "12", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Antipov:2021:PRA, author = "Denis Antipov and Benjamin Doerr", title = "Precise Runtime Analysis for Plateau Functions", journal = j-TELO, volume = "1", number = "4", pages = "13:1--13:28", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3469800", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Mon Apr 18 11:49:32 MDT 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3469800", abstract = "To gain a better theoretical understanding of how evolutionary algorithms (EAs) cope with plateaus of constant fitness, we propose the n -dimensional \textsc {Plateau} _k function as natural benchmark and analyze how different variants of the (1 + 1) EA \ldots{}", acknowledgement = ack-nhfb, articleno = "13", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Lopez-ibanez:2021:REC, author = "Manuel L{\'o}pez-Ib{\'a}{\~n}ez and Juergen Branke and Lu{\'\i}s Paquete", title = "Reproducibility in Evolutionary Computation", journal = j-TELO, volume = "1", number = "4", pages = "14:1--14:21", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3466624", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Mon Apr 18 11:49:32 MDT 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3466624", abstract = "Experimental studies are prevalent in Evolutionary Computation (EC), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scientific fields. In this article, we \ldots{}", acknowledgement = ack-nhfb, articleno = "14", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Khosravi:2021:ECP, author = "Faramarz Khosravi and Alexander Rass and J{\"u}rgen Teich", title = "Efficient Computation of Probabilistic Dominance in Multi-objective Optimization", journal = j-TELO, volume = "1", number = "4", pages = "15:1--15:26", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3469801", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Mon Apr 18 11:49:32 MDT 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3469801", abstract = "Real-world problems typically require the simultaneous optimization of multiple, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective \ldots{}", acknowledgement = ack-nhfb, articleno = "15", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Doerr:2021:SRP, author = "Benjamin Doerr and Frank Neumann", title = "A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization", journal = j-TELO, volume = "1", number = "4", pages = "16:1--16:43", month = dec, year = "2021", CODEN = "????", DOI = "https://doi.org/10.1145/3472304", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Mon Apr 18 11:49:32 MDT 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3472304", abstract = "The theory of evolutionary computation for discrete search spaces has made significant progress since the early 2010s. This survey summarizes some of the most important recent results in this research area. It discusses fine-grained models of runtime \ldots{}", acknowledgement = ack-nhfb, articleno = "16", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Mittal:2022:LBI, author = "Sukrit Mittal and Dhish Kumar Saxena and Kalyanmoy Deb and Erik D. Goodman", title = "A Learning-based Innovized Progress Operator for Faster Convergence in Evolutionary Multi-objective Optimization", journal = j-TELO, volume = "2", number = "1", pages = "1:1--1:29", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3474059", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Mon Apr 18 11:49:32 MDT 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3474059", abstract = "Learning effective problem information from already explored search space in an optimization run, and utilizing it to improve the convergence of subsequent solutions, have represented important directions in Evolutionary Multi-objective Optimization (EMO) \ldots{}", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Akimoto:2022:SPO, author = "Youhei Akimoto and Yoshiki Miyauchi and Atsuo Maki", title = "Saddle Point Optimization with Approximate Minimization Oracle and Its Application to Robust Berthing Control", journal = j-TELO, volume = "2", number = "1", pages = "2:1--2:32", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510425", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Mon Apr 18 11:49:32 MDT 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3510425", abstract = "We propose an approach to saddle point optimization relying only on oracles that solve minimization problems approximately. We analyze its convergence property on a strongly convex-concave problem and show its linear convergence toward the global min-max \ldots{}", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Wang:2022:IDP, author = "Hao Wang and Diederick Vermetten and Furong Ye and Carola Doerr and Thomas B{\"a}ck", title = "{IOHanalyzer}: Detailed Performance Analyses for Iterative Optimization Heuristics", journal = j-TELO, volume = "2", number = "1", pages = "3:1--3:29", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3510426", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Mon Apr 18 11:49:32 MDT 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3510426", abstract = "Benchmarking and performance analysis play an important role in understanding the behaviour of iterative optimization heuristics (IOHs) such as local search algorithms, genetic and evolutionary algorithms, Bayesian optimization algorithms, etc. This task, \ldots{}", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Chang:2022:RTM, author = "Yi-Hsiang Chang and Kuan-Yu Chang and Henry Kuo and Chun-Yi Lee", title = "Reusability and Transferability of Macro Actions for Reinforcement Learning", journal = j-TELO, volume = "2", number = "1", pages = "4:1--4:16", month = mar, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3514260", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Mon Apr 18 11:49:32 MDT 2022", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3514260", abstract = "Conventional reinforcement learning (RL) typically determines an appropriate primitive action at each timestep. However, by using a proper macro action, defined as a sequence of primitive actions, an RL agent is able to bypass intermediate states to a \ldots{}", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Mathias:2022:AER, author = "H. David Mathias and Annie S. Wu and Daniel Dang", title = "Analysis of Evolved Response Thresholds for Decentralized Dynamic Task Allocation", journal = j-TELO, volume = "2", number = "2", pages = "5:1--5:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3530821", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:08 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3530821", abstract = "In this work, we investigate the application of a multi-objective genetic algorithm to the problem of task allocation in a self-organizing, decentralized, threshold-based swarm. We use a multi-objective genetic algorithm to evolve response thresholds for \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "5", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Langdon:2022:DGP, author = "William B. Langdon", title = "Deep Genetic Programming Trees Are Robust", journal = j-TELO, volume = "2", number = "2", pages = "6:1--6:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3539738", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:08 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3539738", abstract = "We sample the genetic programming tree search space and show it is smooth, since many mutations on many test cases have little or no fitness impact. We generate uniformly at random high-order polynomials composed of 12,500 and 750,000 additions and \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "6", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Rainford:2022:CDS, author = "Penny Faulkner Rainford and Barry Porter", title = "Code and Data Synthesis for Genetic Improvement in Emergent Software Systems", journal = j-TELO, volume = "2", number = "2", pages = "7:1--7:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3542823", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:08 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3542823", abstract = "Emergent software systems are assembled from a collection of small code blocks, where some of those blocks have alternative implementation variants; they optimise at run-time by learning which compositions of alternative blocks best suit each deployment \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "7", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Binois:2022:SHD, author = "Micka{\"e}l Binois and Nathan Wycoff", title = "A Survey on High-dimensional {Gaussian} Process Modeling with Application to {Bayesian} Optimization", journal = j-TELO, volume = "2", number = "2", pages = "8:1--8:??", month = jun, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3545611", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:08 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3545611", abstract = "Bayesian Optimization (BO), the application of Bayesian function approximation to finding optima of expensive functions, has exploded in popularity in recent years. In particular, much attention has been paid to improving its efficiency on problems with \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "8", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Spettel:2022:DMA, author = "Patrick Spettel and Hans-Georg Beyer", title = "On the Design of a Matrix Adaptation Evolution Strategy for Optimization on General Quadratic Manifolds", journal = j-TELO, volume = "2", number = "3", pages = "9:1--9:??", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3551394", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:08 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3551394", abstract = "An evolution strategy design is presented that allows for an evolution on general quadratic manifolds. That is, it covers elliptic, parabolic, and hyperbolic equality constraints. The peculiarity of the presented algorithm design is that it is an interior \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "9", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Pushak:2022:ALL, author = "Yasha Pushak and Holger Hoos", title = "{AutoML} Loss Landscapes", journal = j-TELO, volume = "2", number = "3", pages = "10:1--10:??", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3558774", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:08 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3558774", abstract = "As interest in machine learning and its applications becomes more widespread, how to choose the best models and hyper-parameter settings becomes more important. This problem is known to be challenging for human experts, and consequently, a growing number \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "10", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Do:2022:AED, author = "Anh Do and Mingyu Guo and Aneta Neumann and Frank Neumann", title = "Analysis of Evolutionary Diversity Optimization for Permutation Problems", journal = j-TELO, volume = "2", number = "3", pages = "11:1--11:??", month = sep, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3561974", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:08 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3561974", abstract = "Generating diverse populations of high-quality solutions has gained interest as a promising extension to the traditional optimization tasks. This work contributes to this line of research with an investigation on evolutionary diversity optimization for \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "11", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Wild:2022:MDN, author = "Alexander Wild and Barry Porter", title = "Multi-donor Neural Transfer Learning for Genetic Programming", journal = j-TELO, volume = "2", number = "4", pages = "12:1--12:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3563043", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:09 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3563043", abstract = "Genetic programming (GP), for the synthesis of brand new programs, continues to demonstrate increasingly capable results towards increasingly complex problems. A key challenge in GP is how to learn from the past so that the successful synthesis of simple \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "12", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Fajardo:2022:TEA, author = "Mario Alejandro Hevia Fajardo and Dirk Sudholt", title = "Theoretical and Empirical Analysis of Parameter Control Mechanisms in the $ (1 + (\lambda, \lambda)) $ Genetic Algorithm", journal = j-TELO, volume = "2", number = "4", pages = "13:1--13:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3564755", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:09 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3564755", abstract = "The self-adjusting$ (1 + (\lambda, \lambda)) $ GA is the best known genetic algorithm for problems with a good fitness-distance correlation as in OneMax. It uses a parameter control mechanism for the parameter $ \lambda $ that governs the mutation strength and the number of \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "13", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Filho:2022:ERP, author = "Renato Miranda Filho and An{\'\i}sio M. Lacerda and Gisele L. Pappa", title = "Explainable Regression Via Prototypes", journal = j-TELO, volume = "2", number = "4", pages = "14:1--14:??", month = dec, year = "2022", CODEN = "????", DOI = "https://doi.org/10.1145/3576903", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:09 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3576903", abstract = "Model interpretability/explainability is increasingly a concern when applying machine learning to real-world problems. In this article, we are interested in explaining regression models by exploiting prototypes, which are exemplar cases in the problem \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "14", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Flageat:2023:EAP, author = "Manon Flageat and F{\'e}lix Chalumeau and Antoine Cully", title = "Empirical analysis of {PGA-MAP-Elites} for Neuroevolution in Uncertain Domains", journal = j-TELO, volume = "3", number = "1", pages = "1:1--1:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3577203", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:09 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3577203", abstract = "Quality-Diversity algorithms, among which are the Multi-dimensional Archive of Phenotypic Elites (MAP-Elites), have emerged as powerful alternatives to performance-only optimisation approaches as they enable generating collections of diverse and high-. \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "1", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Bradley:2023:GVC, author = "James R. Bradley and A. Paul Blossom", title = "The Generation of Visually Credible Adversarial Examples with Genetic Algorithms", journal = j-TELO, volume = "3", number = "1", pages = "2:1--2:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3582276", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:09 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3582276", abstract = "An adversarial example is an input that a neural network misclassifies although the input differs only slightly from an input that the network classifies correctly. Adversarial examples are used to augment neural network training data, measure the \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "2", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Pigozzi:2023:FID, author = "Federico Pigozzi and Eric Medvet and Alberto Bartoli and Marco Rochelli", title = "Factors Impacting Diversity and Effectiveness of Evolved Modular Robots", journal = j-TELO, volume = "3", number = "1", pages = "3:1--3:??", month = mar, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3587101", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:09 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3587101", abstract = "In many natural environments, different forms of living organisms successfully accomplish the same task while being diverse in shape and behavior. This biodiversity is what made life capable of adapting to disrupting changes. Being able to reproduce \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "3", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Fieldsend:2023:EBG, author = "Jonathan Fieldsend and Markus Wagner", title = "Editorial to the {``Best of GECCO 2022''} Special Issue: Part {I}", journal = j-TELO, volume = "3", number = "2", pages = "4:1--4:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3606034", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:09 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3606034", acknowledgement = ack-nhfb, ajournal = "", articleno = "4", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Friedrich:2023:CCC, author = "Tobias Friedrich and Timo K{\"o}tzing and Aishwarya Radhakrishnan and Leon Schiller and Martin Schirneck and Georg Tennigkeit and Simon Wietheger", title = "Crossover for Cardinality Constrained Optimization", journal = j-TELO, volume = "3", number = "2", pages = "5:1--5:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3603629", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:09 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3603629", abstract = "To understand better how and why crossover can benefit constrained optimization, we consider pseudo-Boolean functions with an upper bound B on the number of 1-bits allowed in the length- n bit string (i.e., a cardinality constraint). We investigate the \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "5", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Allard:2023:ODR, author = "Maxime Allard and Sim{\'o}n C. Smith and Konstantinos Chatzilygeroudis and Bryan Lim and Antoine Cully", title = "Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity", journal = j-TELO, volume = "3", number = "2", pages = "6:1--6:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3596912", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:09 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3596912", abstract = "In real-world environments, robots need to be resilient to damages and robust to unforeseen scenarios. Quality-Diversity (QD) algorithms have been successfully used to make robots adapt to damages in seconds by leveraging a diverse set of learned skills. \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "6", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{OlivettiDeFranca:2023:TIR, author = "Fabr{\'\i}cio {Olivetti De Fran{\c{c}}a}", title = "Transformation-Interaction-Rational Representation for Symbolic Regression: a Detailed Analysis of {SRBench} Results", journal = j-TELO, volume = "3", number = "2", pages = "7:1--7:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3597312", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:09 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3597312", abstract = "Symbolic Regression searches for a parametric model with the optimal value of the parameters that best fits a set of samples to a measured target. The desired solution has a balance between accuracy and interpretability. Commonly, there is no constraint \ldots{}", acknowledgement = ack-nhfb, ajournal = "", articleno = "7", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Miyagi:2023:CMA, author = "Atsuhiro Miyagi and Yoshiki Miyauchi and Atsuo Maki and Kazuto Fukuchi and Jun Sakuma and Youhei Akimoto", title = "Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min-Max Optimization and Its Application to Berthing Control Tasks", journal = j-TELO, volume = "3", number = "2", pages = "8:1--8:??", month = jun, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3603716", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Fri Aug 25 12:08:09 MDT 2023", bibsource = "http://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3603716", abstract = "In this study, we consider a continuous min-max optimization problem \ldots{} whose objective function is a black-box. We propose a novel approach to minimize the worst-case objective function \ldots{} directly using a \ldots{}.", acknowledgement = ack-nhfb, ajournal = "", articleno = "8", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Gaier:2023:EER, author = "Adam Gaier and Giuseppe Paolo and Antoine Cully", title = "Editorial to the {``Evolutionary Reinforcement Learning''} Special Issue", journal = j-TELO, volume = "3", number = "3", pages = "9:1--9:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3624559", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:43 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3624559", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Sigaud:2023:CED, author = "Olivier Sigaud", title = "Combining Evolution and Deep Reinforcement Learning for Policy Search: a Survey", journal = j-TELO, volume = "3", number = "3", pages = "10:1--10:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3569096", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:43 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3569096", abstract = "Deep neuroevolution and deep Reinforcement Learning have received a lot of attention over the past few years. Some works have compared them, highlighting their pros and cons, but an emerging trend combines them so as to benefit from the best of both \ldots{}", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Timilsina:2023:PET, author = "Ashutosh Timilsina and Simone Silvestri", title = "{P2P} Energy Trading through Prospect Theory, Differential Evolution, and Reinforcement Learning", journal = j-TELO, volume = "3", number = "3", pages = "11:1--11:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3603148", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:43 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3603148", abstract = "Peer-to-peer (P2P) energy trading is a decentralized energy market where local energy prosumers act as peers, trading energy among each other. Existing works in this area largely overlook the importance of user behavioral modeling and assume users' \ldots{}", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{LeTolguenec:2023:CCD, author = "Paul-Antoine {Le Tolguenec} and Emmanuel Rachelson and Yann Besse and Dennis G. Wilson", title = "Curiosity Creates Diversity in Policy Search", journal = j-TELO, volume = "3", number = "3", pages = "12:1--12:??", month = sep, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3605782", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:43 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3605782", abstract = "When searching for policies, reward-sparse environments often lack sufficient information about which behaviors to improve upon or avoid. In such environments, the policy search process is bound to blindly search for reward-yielding transitions and no \ldots{}", acknowledgement = ack-nhfb, articleno = "12", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Renzullo:2023:ESC, author = "Joseph Renzullo and Westley Weimer and Stephanie Forrest", title = "Evolving Software: Combining Online Learning with Mutation-Based Stochastic Search", journal = j-TELO, volume = "3", number = "4", pages = "13:1--13:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3597617", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:43 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3597617", abstract = "Evolutionary algorithms and related mutation-based methods have been used in software engineering, with recent emphasis on the problem of repairing bugs. In this work, programs are typically not synthesized from a random start. Instead, existing solutions-. \ldots{}", acknowledgement = ack-nhfb, articleno = "13", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Yazdani:2023:SBP, author = "Delaram Yazdani and Danial Yazdani and Donya Yazdani and Mohammad Nabi Omidvar and Amir H. Gandomi and Xin Yao", title = "A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems", journal = j-TELO, volume = "3", number = "4", pages = "14:1--14:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3604812", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:43 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3604812", abstract = "Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most \ldots{}", acknowledgement = ack-nhfb, articleno = "14", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Ceberio:2023:MBG, author = "Josu Ceberio and Valentino Santucci", title = "Model-based Gradient Search for Permutation Problems", journal = j-TELO, volume = "3", number = "4", pages = "15:1--15:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3628605", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:43 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3628605", abstract = "Global random search algorithms are characterized by using probability distributions to optimize problems. Among them, generative methods iteratively update the distributions by using the observations sampled. For instance, this is the case of the well-. \ldots{}", acknowledgement = ack-nhfb, articleno = "15", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Karl:2023:MOH, author = "Florian Karl and Tobias Pielok and Julia Moosbauer and Florian Pfisterer and Stefan Coors and Martin Binder and Lennart Schneider and Janek Thomas and Jakob Richter and Michel Lang and Eduardo C. Garrido-Merch{\'a}n and Juergen Branke and Bernd Bischl", title = "Multi-Objective Hyperparameter Optimization in Machine Learning --- an Overview", journal = j-TELO, volume = "3", number = "4", pages = "16:1--16:??", month = dec, year = "2023", CODEN = "????", DOI = "https://doi.org/10.1145/3610536", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:43 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3610536", abstract = "Hyperparameter optimization constitutes a large part of typical modern machine learning (ML) workflows. This arises from the fact that ML methods and corresponding preprocessing steps often only yield optimal performance when hyperparameters are properly \ldots{}", acknowledgement = ack-nhfb, articleno = "16", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Bacardit:2024:ISI, author = "Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and John McCall and David Walker", title = "Introduction to the Special Issue on Explainable {AI} in Evolutionary Computation", journal = j-TELO, volume = "4", number = "1", pages = "1:1--1:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3649144", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3649144", acknowledgement = ack-nhfb, articleno = "1", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Wang:2024:MOF, author = "Ziming Wang and Changwu Huang and Yun Li and Xin Yao", title = "Multi-objective Feature Attribution Explanation For Explainable Machine Learning", journal = j-TELO, volume = "4", number = "1", pages = "2:1--2:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3617380", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3617380", abstract = "The feature attribution-based explanation (FAE) methods, which indicate how much each input feature contributes to the model's output for a given data point, are one of the most popular categories of explainable machine learning techniques. Although \ldots{}", acknowledgement = ack-nhfb, articleno = "2", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Banda:2024:MOE, author = "Tiwonge Msulira Banda and Alexandru-Ciprian Zavoianu and Andrei Petrovski and Daniel W{\"o}ckinger and Gerd Bramerdorfer", title = "A Multi-Objective Evolutionary Approach to Discover Explainability Tradeoffs when Using Linear Regression to Effectively Model the Dynamic Thermal Behaviour of Electrical Machines", journal = j-TELO, volume = "4", number = "1", pages = "3:1--3:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3597618", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3597618", abstract = "Modelling and controlling heat transfer in rotating electrical machines is very important as it enables the design of assemblies (e.g., motors) that are efficient and durable under multiple operational scenarios. To address the challenge of deriving \ldots{}", acknowledgement = ack-nhfb, articleno = "3", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Misitano:2024:EEA, author = "Giovanni Misitano", title = "Exploring the Explainable Aspects and Performance of a Learnable Evolutionary Multiobjective Optimization Method", journal = j-TELO, volume = "4", number = "1", pages = "4:1--4:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3626104", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3626104", abstract = "Multiobjective optimization problems have multiple conflicting objective functions to be optimized simultaneously. The solutions to these problems are known as Pareto optimal solutions, which are mathematically incomparable. Thus, a decision maker must be \ldots{}", acknowledgement = ack-nhfb, articleno = "4", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Nadizar:2024:AIL, author = "Giorgia Nadizar and Luigi Rovito and Andrea {De Lorenzo} and Eric Medvet and Marco Virgolin", title = "An Analysis of the Ingredients for Learning Interpretable Symbolic Regression Models with Human-in-the-loop and Genetic Programming", journal = j-TELO, volume = "4", number = "1", pages = "5:1--5:??", month = mar, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3643688", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Apr 30 10:43:44 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3643688", abstract = "Interpretability is a critical aspect to ensure a fair and responsible use of machine learning (ML) in high-stakes applications. Genetic programming (GP) has been used to obtain interpretable ML models because it operates at the level of functional \ldots{}", acknowledgement = ack-nhfb, articleno = "5", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Fieldsend:2024:IBG, author = "Jonathan Fieldsend and Markus Wagner", title = "Introduction to the {``Best of GECCO 2022''} Special Issue: {Part II}", journal = j-TELO, volume = "4", number = "2", pages = "6:1--6:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3665797", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3665797", acknowledgement = ack-nhfb, articleno = "6", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Tinos:2024:ILS, author = "Renato Tin{\'o}s and Michal W. Przewozniczek and Darrell Whitley and Francisco Chicano", title = "Iterated Local Search with Linkage Learning", journal = j-TELO, volume = "4", number = "2", pages = "7:1--7:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3651165", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3651165", abstract = "In pseudo-Boolean optimization, a variable interaction graph represents variables as vertices, and interactions between pairs of variables as edges. In black-box optimization, the variable interaction graph may be at least partially discovered by using \ldots{}", acknowledgement = ack-nhfb, articleno = "7", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Lavinas:2024:MEC, author = "Yuri Lavinas and Marcelo Ladeira and Gabriela Ochoa and Claus Aranha", title = "Multiobjective Evolutionary Component Effect on Algorithm Behaviour", journal = j-TELO, volume = "4", number = "2", pages = "8:1--8:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3612933", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3612933", abstract = "The performance of multiobjective evolutionary algorithms (MOEAs) varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there \ldots{}", acknowledgement = ack-nhfb, articleno = "8", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Long:2024:GCR, author = "Fu Xing Long and Bas van Stein and Moritz Frenzel and Peter Krause and Markus Gitterle and Thomas B{\"a}ck", title = "Generating Cheap Representative Functions for Expensive Automotive Crashworthiness Optimization", journal = j-TELO, volume = "4", number = "2", pages = "9:1--9:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3646554", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3646554", abstract = "Solving real-world engineering optimization problems, such as automotive crashworthiness optimization, is extremely challenging, because the problem characteristics are oftentimes not well understood. Furthermore, typical hyperparameter optimization (HPO) \ldots{}", acknowledgement = ack-nhfb, articleno = "9", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Hamano:2024:MPB, author = "Ryoki Hamano and Shota Saito and Masahiro Nomura and Shinichi Shirakawa", title = "Marginal Probability-Based Integer Handling for {CMA-ES} Tackling Single- and Multi-Objective Mixed-Integer Black-Box Optimization", journal = j-TELO, volume = "4", number = "2", pages = "10:1--10:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3632962", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3632962", abstract = "This study targets the mixed-integer black-box optimization (MI-BBO) problem where continuous and integer variables should be optimized simultaneously. The covariance matrix adaptation evolution strategy (CMA-ES), our focus in this study, is a population-. \ldots{}", acknowledgement = ack-nhfb, articleno = "10", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Aboutaib:2024:INM, author = "Brahim Aboutaib and Andrew M. Sutton", title = "The Influence of Noise on Multi-parent Crossover for an {Island} Model Genetic Algorithm", journal = j-TELO, volume = "4", number = "2", pages = "11:1--11:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3630638", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3630638", abstract = "Many optimization problems tackled by evolutionary algorithms are not only computationally expensive but also complicated, with one or more sources of noise. One technique to deal with high computational overhead is parallelization. However, though the \ldots{}", acknowledgement = ack-nhfb, articleno = "11", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Nikfarjam:2024:UQD, author = "Adel Nikfarjam and Aneta Neumann and Frank Neumann", title = "On the Use of Quality Diversity Algorithms for the Travelling Thief Problem", journal = j-TELO, volume = "4", number = "2", pages = "12:1--12:??", month = jun, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3641109", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:54 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3641109", abstract = "In real-world optimisation, it is common to face several sub-problems interacting and forming the main problem. There is an inter-dependency between the sub-problems, making it impossible to solve such a problem by focusing on only one component. The \ldots{}", acknowledgement = ack-nhfb, articleno = "12", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Cai:2024:PIM, author = "Xu Cai and Yu Xue", title = "A Population Initialization Method Based on Similarity and Mutual Information in Evolutionary Algorithm for Bi-Objective Feature Selection", journal = j-TELO, volume = "4", number = "3", pages = "13:1--13:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3653025", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3653025", abstract = "Feature selection (FS) is an important data pre-processing technique in classification. It aims to remove redundant and irrelevant features from the data, which reduces the dimensionality of data and improves the performance of the classifier. Thus, FS is \ldots{}", acknowledgement = ack-nhfb, articleno = "13", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Arza:2024:GES, author = "Etor Arza and L{\'e}ni K. {Le Goff} and Emma Hart", title = "Generalized Early Stopping in Evolutionary Direct Policy Search", journal = j-TELO, volume = "4", number = "3", pages = "14:1--14:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3653024", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3653024", abstract = "Lengthy evaluation times are common in many optimization problems such as direct policy search tasks, especially when they involve conducting evaluations in the physical world, for example, in robotics applications. Often when evaluating solution over a \ldots{}", acknowledgement = ack-nhfb, articleno = "14", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Garmendia:2024:ANC, author = "Andoni I. Garmendia and Josu Ceberio and Alexander Mendiburu", title = "Applicability of Neural Combinatorial Optimization: a Critical View", journal = j-TELO, volume = "4", number = "3", pages = "15:1--15:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3647644", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3647644", abstract = "Neural Combinatorial Optimization has emerged as a new paradigm in the optimization area. It attempts to solve optimization problems by means of neural networks and reinforcement learning. In the past few years, due to their novelty and presumably good \ldots{}", acknowledgement = ack-nhfb, articleno = "15", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Liu:2024:EIA, author = "Shengcai Liu and Ning Lu and Wenjing Hong and Chao Qian and Ke Tang", title = "Effective and Imperceptible Adversarial Textual Attack Via Multi-objectivization", journal = j-TELO, volume = "4", number = "3", pages = "16:1--16:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3651166", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3651166", abstract = "The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, \ldots{}", acknowledgement = ack-nhfb, articleno = "16", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Santoni:2024:CHD, author = "Maria Laura Santoni and Elena Raponi and Renato {De Leone} and Carola Doerr", title = "Comparison of High-Dimensional {Bayesian} Optimization Algorithms on {BBOB}", journal = j-TELO, volume = "4", number = "3", pages = "17:1--17:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3670683", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3670683", abstract = "Bayesian Optimization (BO) is a class of surrogate-based black-box optimization heuristics designed to efficiently locate high-quality solutions for problems that are expensive to evaluate, and therefore allow only small evaluation budgets. BO is \ldots{}", acknowledgement = ack-nhfb, articleno = "17", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } @Article{Malagon:2024:COF, author = "Mikel Malag{\'o}n and Ekhine Irurozki and Josu Ceberio", title = "A Combinatorial Optimization Framework for Probability-Based Algorithms by Means of Generative Models", journal = j-TELO, volume = "4", number = "3", pages = "18:1--18:??", month = sep, year = "2024", CODEN = "????", DOI = "https://doi.org/10.1145/3665650", ISSN = "2688-299X (print), 2688-3007 (electronic)", ISSN-L = "2688-299X", bibdate = "Tue Aug 20 09:39:55 MDT 2024", bibsource = "https://www.math.utah.edu/pub/tex/bib/telo.bib", URL = "https://dl.acm.org/doi/10.1145/3665650", abstract = "Probability-based algorithms have proven to be a solid alternative for approaching optimization problems. Nevertheless, in many cases, using probabilistic models that efficiently exploit the characteristics of the problem involves large computational \ldots{}", acknowledgement = ack-nhfb, articleno = "18", fjournal = "ACM Transactions on Evolutionary Learning and Optimization", journal-URL = "https://dl.acm.org/loi/telo", } %%% [20-Aug-2024] check incomplete v4n3