@Preamble{"\input bibnames.sty" #
"\ifx \undefined \booktitle \def \booktitle #1{{{\em #1}}} \fi" #
"\ifx \undefined \TM \def \TM {${}^{\sc TM}$} \fi"
}
@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/|"}
@String{j-TELO = "ACM Transactions on Evolutionary
Learning and Optimization (TELO)"}
@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",
}
