Search Results - "Evolutionary algorithms, genetic algorithms (computational aspects)"

Self-adjusting offspring population sizes outperform fixed parameters on the cliff function

Authors: Mario Alejandro Hevia Fajardo Dirk Sudholt

Source: Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms. :1-15

Subject Terms: Evolutionary algorithms, genetic algorithms (computational aspects), 0202 electrical engineering, electronic engineering, information engineering, Analysis of algorithms, 0102 computer and information sciences, 02 engineering and technology, Approximation methods and heuristics in mathematical programming, 01 natural sciences

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Access URL: https://dblp.uni-trier.de/db/conf/foga/foga2021.html#FajardoS21

An incremental-learning model-based multiobjective estimation of distribution algorithm

Authors: Shenmin Song Liguo Tan Tingrui Liu et al.

Source: Information Sciences. 569:430-449

Subject Terms: estimation of distribution, incremental learning, 0209 industrial biotechnology, evolutionary algorithm, Gaussian mixture model, Evolutionary algorithms, genetic algorithms (computational aspects), Learning and adaptive systems in artificial intelligence, 0202 electrical engineering, electronic engineering, information engineering, multiobjective optimization, 02 engineering and technology, Approximation methods and heuristics in mathematical programming, Multi-objective and goal programming

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Access URL: https://zbmath.org/7768929
https://doi.org/10.1016/j.ins.2021.04.011
https://dblp.uni-trier.de/db/journals/isci/isci569.html#LiuLTS21
https://www.sciencedirect.com/science/article/abs/pii/S0020025521003327
https://doi.org/10.1016/j.ins.2021.04.011

Fixed-target runtime analysis

Authors: Buzdalov, Maxim Doerr, Benjamin Doerr, Carola et al.

Contributors: Buzdalov, Maxim Doerr, Benjamin Doerr, Carola et al.

Source: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. :1295-1303

Subject Terms: FOS: Computer and information sciences, Evolutionary algorithms, genetic algorithms (computational aspects), [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], randomized search heuristics, Computer Science - Neural and Evolutionary Computing, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], 68Q17, evolutionary computation, Analysis of algorithms, Neural and Evolutionary Computing (cs.NE), F.2.2, Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.), runtime analysis

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Access URL: http://arxiv.org/pdf/2004.09613
http://arxiv.org/abs/2004.09613
https://zbmath.org/7535652
https://doi.org/10.1007/s00453-021-00881-0
https://link.springer.com/article/10.1007%2Fs00453-021-00881-0
https://hal.archives-ouvertes.fr/hal-03377095
https://dblp.uni-trier.de/db/journals/corr/corr2004.html#abs-2004-09613
https://arxiv.org/abs/2004.09613
https://arxiv.org/abs/2004.09613
https://ui.adsabs.harvard.edu/abs/2020arXiv200409613B/abstract
http://arxiv.org/pdf/2004.09613.pdf
https://hal.sorbonne-universite.fr/hal-02871956v1

Self-Adjusting Mutation Rates with Provably Optimal Success Rules: Self-adjusting mutation rates with provably optimal success rules

Authors: Doerr, Benjamin Doerr, Carola Lengler, Johannes et al.

Contributors: Doerr, Benjamin Doerr, Carola Lengler, Johannes et al.

Source: Algorithmica. 83:3108-3147

Subject Terms: FOS: Computer and information sciences, Evolutionary algorithms, genetic algorithms (computational aspects), Randomized algorithms, [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], randomized search heuristics, parameter control, Computer Science - Neural and Evolutionary Computing, 0102 computer and information sciences, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], 01 natural sciences, evolutionary computation, 0202 electrical engineering, electronic engineering, information engineering, Analysis of algorithms, Neural and Evolutionary Computing (cs.NE), runtime analysis

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Access URL: https://hal.sorbonne-universite.fr/hal-02175768/file/bl%20pap162s3-file1%20%281%29.pdf
http://arxiv.org/abs/1902.02588
https://zbmath.org/7421502
https://doi.org/10.1007/s00453-021-00854-3
https://dumas.ccsd.cnrs.fr/LIP6/hal-02175768
http://arxiv.org/pdf/1902.02588.pdf
https://dblp.uni-trier.de/db/conf/gecco/gecco2019.html#DoerrDL19
https://hal.archives-ouvertes.fr/hal-02175768
https://hal.archives-ouvertes.fr/hal-03377092
https://dblp.uni-trier.de/db/journals/algorithmica/algorithmica83.html#DoerrDL21
https://link.springer.com/article/10.1007/s00453-021-00854-3
https://hal-polytechnique.archives-ouvertes.fr/hal-03377092v1
https://hal.sorbonne-universite.fr/hal-03377092v1
https://hal.sorbonne-universite.fr/hal-03377092v1/document
https://doi.org/10.1007/s00453-021-00854-3

A new taxonomy of global optimization algorithms

Authors: Jörg Stork A. E. Eiben Thomas Bartz-Beielstein

Source: Natural Computing. 21:219-242

Subject Terms: ddc:004, FOS: Computer and information sciences, Evolutionary algorithms, genetic algorithms (computational aspects), ddc:000, 0211 other engineering and technologies, 02 engineering and technology, Nonconvex programming, global optimization, Evolutionärer Algorithmus, taxonomy, 0202 electrical engineering, electronic engineering, information engineering, Globale Optimierung, surrogate, Neural and Evolutionary Computing (cs.NE), metaheuristics, Computer Science - Neural and Evolutionary Computing, hybrid optimization, Taxonomie, Approximation methods and heuristics in mathematical programming, 3. Good health, Metaheuristik, evolutionary computation, Fakultät für Informatik und Ingenieurwissenschaften (F10)

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Access URL: https://link.springer.com/content/pdf/10.1007/s11047-020-09820-4.pdf
http://arxiv.org/abs/1808.08818
https://zbmath.org/7733308
https://doi.org/10.1007/s11047-020-09820-4
https://research.vu.nl/en/publications/f4ff540b-8c20-4af3-95ca-f47e3e6b2075
https://doi.org/10.1007/s11047-020-09820-4
https://hdl.handle.net/1871.1/f4ff540b-8c20-4af3-95ca-f47e3e6b2075
https://link.springer.com/article/10.1007/s11047-020-09820-4
https://link.springer.com/content/pdf/10.1007/s11047-020-09820-4.pdf
https://research.vu.nl/en/publications/a-new-taxonomy-of-global-optimization-algorithms
https://arxiv.org/pdf/1808.08818
https://dblp.uni-trier.de/db/journals/corr/corr1808.html#abs-1808-08818
https://arxiv.org/abs/1808.08818
https://cos.bibl.th-koeln.de/files/753/Stor16acos-submitted.pdf
https://cos.bibl.th-koeln.de/frontdoor/index/index/searchtype/all/start/0/rows/20/languagefq/eng/subjectfq/Globale+Optimierung/docId/753
http://ui.adsabs.harvard.edu/abs/2018arXiv180808818S/abstract
https://cos.bibl.th-koeln.de/files/753/Stor16acos-submitted.pdf
https://epb.bibl.th-koeln.de/files/2246/s11047-020-09820-4.pdf

Tail bounds on hitting times of randomized search heuristics using variable drift analysis

Authors: Lehre, P. K. Witt, C.

Source: Lehre, P K & Witt, C 2021, ' Tail bounds on hitting times of randomized search heuristics using variable drift analysis ', Combinatorics Probability and Computing, vol. 30, no. 4, pp. 550–569 . https://doi.org/10.1017/S0963548320000565

Subject Terms: Evolutionary algorithms, genetic algorithms (computational aspects), 4. Education, Randomized algorithms, 0202 electrical engineering, electronic engineering, information engineering, 0102 computer and information sciences, 02 engineering and technology, Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.), 01 natural sciences

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Access URL: https://backend.orbit.dtu.dk/ws/files/234770969/tail_bounds_on_hitting_times_of_randomized_search_heuristics_using_variable_drift_analysis.pdf
https://orbit.dtu.dk/en/publications/tail-bounds-on-hitting-times-of-randomized-search-heuristics-usin
https://backend.orbit.dtu.dk/ws/files/234770969/tail_bounds_on_hitting_times_of_randomized_search_heuristics_using_variable_drift_analysis.pdf
https://www.cambridge.org/core/journals/combinatorics-probability-and-computing/article/tail-bounds-on-hitting-times-of-randomized-search-heuristics-using-variable-drift-analysis/F30499B93D4498D404E00206005A999A
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/F30499B93D4498D404E00206005A999A/S0963548320000565a.pdf/div-class-title-tail-bounds-on-hitting-times-of-randomized-search-heuristics-using-variable-drift-analysis-div.pdf
https://orbit.dtu.dk/en/publications/bfdcc357-ac66-4d8d-bb15-c5701c9ed438

Novel biogeography-based optimization algorithm with hybrid migration and global-best Gaussian mutation

Authors: Zihao Fu Xinming Zhang Shangwang Liu et al.

Source: Applied Mathematical Modelling. 86:74-91

Subject Terms: evolutionary algorithm, biogeography-based optimization algorithm, Evolutionary algorithms, genetic algorithms (computational aspects), \(K\)-menas clustering, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Gaussian mutation, 02 engineering and technology, minimum spanning tree, migration, Approximation methods and heuristics in mathematical programming

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Access URL: https://www.sciencedirect.com/science/article/pii/S0307904X20302420

Self-adjusting evolutionary algorithms for multimodal optimization

Authors: Amirhossein Rajabi Carsten Witt

Source: Rajabi, A & Witt, C 2020, Self-adjusting evolutionary algorithms for multimodal optimization . in Proceedings of the 2020 Genetic and Evolutionary Computation Conference . Association for Computing Machinery, pp. 1314-1322, 2020 Genetic and Evolutionary Computation Conference, Cancun, Mexico, 08/07/2020 . https://doi.org/10.1145/3377930.3389833

Subject Terms: multimodal functions, FOS: Computer and information sciences, Evolutionary algorithms, genetic algorithms (computational aspects), Multimodal functions, randomized search heuristics, self-adjusting algorithms, Computer Science - Neural and Evolutionary Computing, 0102 computer and information sciences, 02 engineering and technology, 01 natural sciences, Self-adjusting algorithms, Randomised search heuristics, 0202 electrical engineering, electronic engineering, information engineering, Analysis of algorithms, Runtime analysis, Neural and Evolutionary Computing (cs.NE), Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.), runtime analysis

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Access URL: https://backend.orbit.dtu.dk/ws/files/247787189/Rajabi_Witt2020_GECCO.pdf
http://arxiv.org/abs/2004.03266
https://arxiv.org/abs/2004.03266
https://ui.adsabs.harvard.edu/abs/2020arXiv200403266R/abstract
https://arxiv.org/pdf/2004.03266.pdf
https://dblp.uni-trier.de/db/journals/corr/corr2004.html#abs-2004-03266
https://dl.acm.org/doi/pdf/10.1145/3377930.3389833
https://orbit.dtu.dk/en/publications/2f0d9a20-b131-4308-9724-13ebf8c68d76

Does comma selection help to cope with local optima?

Authors: Benjamin Doerr École polytechnique (X) Institut Polytechnique de Paris (IP Paris) et al.

Contributors: Benjamin Doerr École polytechnique (X) Institut Polytechnique de Paris (IP Paris) et al.

Source: Proceedings of the 2020 Genetic and Evolutionary Computation Conference. :1304-1313

Subject Terms: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Evolutionary algorithms, genetic algorithms (computational aspects), Computer Science - Artificial Intelligence, Computer Science - Data Structures and Algorithms, Computer Science - Neural and Evolutionary Computing, [INFO]Computer Science [cs], Analysis of algorithms, Data Structures and Algorithms (cs.DS), Neural and Evolutionary Computing (cs.NE), Approximation methods and heuristics in mathematical programming

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Access URL: https://dl.acm.org/doi/pdf/10.1145/3377930.3389823
http://arxiv.org/pdf/2004.01274
http://arxiv.org/abs/2004.01274
https://zbmath.org/7535649
https://doi.org/10.1007/s00453-021-00896-7
https://dl.acm.org/doi/pdf/10.1145/3377930.3389823
https://hal.archives-ouvertes.fr/hal-03278163/document
https://dblp.uni-trier.de/db/journals/corr/corr2004.html#abs-2004-01274
https://archivesic.ccsd.cnrs.fr/LIX/hal-03278163v1
https://ui.adsabs.harvard.edu/abs/2020arXiv200401274D/abstract
http://arxiv.org/pdf/2004.01274.pdf
https://arxiv.org/abs/2004.01274

A Tight Runtime Analysis for the $${(\mu + \lambda )}$$ EA: A tight runtime analysis for the \((\mu + \lambda)\) EA

Authors: Denis Antipov Benjamin Doerr National Research University of Information Technologies, Mechanics and Optics St. Petersburg (ITMO) et al.

Contributors: Denis Antipov Benjamin Doerr National Research University of Information Technologies, Mechanics and Optics St. Petersburg (ITMO) et al.

Source: Algorithmica. 83:1054-1095

Subject Terms: Evolutionary algorithms, genetic algorithms (computational aspects), [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], 0202 electrical engineering, electronic engineering, information engineering, Computer Science - Neural and Evolutionary Computing, Analysis of algorithms, 0102 computer and information sciences, 02 engineering and technology, evolutionary algorithms, 01 natural sciences, runtime analysis

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Access URL: http://arxiv.org/pdf/1812.11061
http://arxiv.org/abs/1812.11061
https://hal.ip-paris.fr/hal-03353025v1
https://link.springer.com/article/10.1007/s00453-020-00731-5
https://hal.archives-ouvertes.fr/hal-03353025
https://hal.archives-ouvertes.fr/hal-03353025/document
https://dblp.uni-trier.de/db/journals/algorithmica/algorithmica83.html#AntipovD21

On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms

Authors: Dogan Corus Pietro S. Oliveto

Source: Proceedings of the Genetic and Evolutionary Computation Conference. :1452-1460

Subject Terms: FOS: Computer and information sciences, crossover, Evolutionary algorithms, genetic algorithms (computational aspects), 13. Climate action, population-based algorithms, Computer Science - Neural and Evolutionary Computing, Analysis of algorithms, Neural and Evolutionary Computing (cs.NE), recombination, genetic algorithms, runtime analysis

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Access URL: http://eprints.whiterose.ac.uk/144388/1/1903.10976v1.pdf
https://link.springer.com/content/pdf/10.1007/s00453-020-00743-1.pdf
http://arxiv.org/abs/1903.10976
https://zbmath.org/7272777
https://doi.org/10.1007/s00453-020-00743-1
http://arxiv.org/pdf/1903.10976.pdf
https://arxiv.org/abs/1903.10976
https://dblp.uni-trier.de/db/conf/gecco/gecco2019.html#CorusO19
https://eprints.whiterose.ac.uk/164018/
http://dblp.uni-trier.de/db/journals/corr/corr1903.html#abs-1903-10976
https://dl.acm.org/doi/pdf/10.1145/3321707.3321783
https://dblp.uni-trier.de/db/journals/algorithmica/algorithmica82.html#CorusO20
https://link.springer.com/article/10.1007/s00453-020-00743-1
https://link.springer.com/content/pdf/10.1007/s00453-020-00743-1.pdf

Analyzing randomized search heuristics via stochastic domination

Authors: Benjamin Doerr École polytechnique (X) Institut Polytechnique de Paris (IP Paris) et al.

Contributors: Benjamin Doerr École polytechnique (X) Institut Polytechnique de Paris (IP Paris) et al.

Source: Theoretical Computer Science. 773:115-137

Subject Terms: Evolutionary algorithms, genetic algorithms (computational aspects), Randomized algorithms, 0211 other engineering and technologies, [INFO]Computer Science [cs], Analysis of algorithms, 0102 computer and information sciences, 02 engineering and technology, evolutionary algorithms, Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.), 01 natural sciences, runtime analysis

File Description: application/xml

Access URL: https://www.sciencedirect.com/science/article/am/pii/S0304397518305954
https://arxiv.org/abs/1801.04487
https://dblp.uni-trier.de/db/journals/tcs/tcs773.html#Doerr19
https://ru.arxiv.org/abs/1801.04487
https://core.ac.uk/display/146481142
https://www.sciencedirect.com/science/article/pii/S0304397518305954
https://arxiv.org/pdf/1801.04487

Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization

Authors: Chen, Zuyan Francis, Adam Li, Shuai et al.

Subject Terms: FOS: Computer and information sciences, 68T05: Evolutionary algorithms, genetic algorithms (computational aspects), see also 68T20 and 90C59, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE)

Access URL: http://arxiv.org/abs/2207.14667

Runtime Analysis for Self-adaptive Mutation Rates: Runtime analysis for self-adaptive mutation rates

Authors: Benjamin Doerr Carsten Witt Jing Yang et al.

Contributors: Benjamin Doerr Carsten Witt Jing Yang et al.

Source: Algorithmica. 83:1012-1053

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Evolutionary algorithms, genetic algorithms (computational aspects), Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, Machine Learning (cs.LG), self-adaptive, Artificial Intelligence (cs.AI), Computer Science - Data Structures and Algorithms, [INFO]Computer Science [cs], Analysis of algorithms, Data Structures and Algorithms (cs.DS), Neural and Evolutionary Computing (cs.NE), evolutionary algorithms, runtime analysis

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Access URL: https://backend.orbit.dtu.dk/ws/files/151866568/p1475_doerr.pdf
http://arxiv.org/abs/1811.12824
https://zbmath.org/7331693
https://doi.org/10.1007/s00453-020-00726-2
https://link.springer.com/10.1007/s00453-020-00726-2
https://link.springer.com/article/10.1007/s00453-020-00726-2
https://dblp.uni-trier.de/db/journals/algorithmica/algorithmica83.html#DoerrWY21
https://orbit.dtu.dk/en/publications/runtime-analysis-for-self-adaptive-mutation-rates-2
https://doi.org/10.1007/s00453-020-00726-2
https://dblp.uni-trier.de/db/journals/corr/corr1811.html#abs-1811-12824
http://ui.adsabs.harvard.edu/abs/2018arXiv181112824D/abstract
https://doi.acm.org/10.1145/3205455.3205569
http://dblp.uni-trier.de/db/conf/gecco/gecco2018.html#DoerrW018

Optimal Parameter Choices via Precise Black-Box Analysis: Optimal parameter choices via precise black-box analysis

Authors: Doerr, Benjamin Doerr, Carola Yang, Jing et al.

Contributors: Doerr, Benjamin Doerr, Carola Yang, Jing et al.

Source: Proceedings of the Genetic and Evolutionary Computation Conference 2016. :1123-1130

Subject Terms: FOS: Computer and information sciences, theory of evolutionary computation, Evolutionary algorithms, genetic algorithms (computational aspects), [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], parameter control, Computer Science - Neural and Evolutionary Computing, 0102 computer and information sciences, 02 engineering and technology, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], 01 natural sciences, black-box complexity, 0202 electrical engineering, electronic engineering, information engineering, Analysis of algorithms, Neural and Evolutionary Computing (cs.NE), runtime analysis

File Description: application/xml; application/pdf

Access URL: http://arxiv.org/pdf/1807.03403
https://www.sciencedirect.com/science/article/am/pii/S0304397519304013
http://arxiv.org/abs/1807.03403
https://zbmath.org/7135111
https://doi.org/10.1016/j.tcs.2019.06.014
https://dblp.uni-trier.de/db/journals/corr/corr1807.html#abs-1807-03403
https://hal.archives-ouvertes.fr/hal-01363930
http://arxiv.org/pdf/1807.03403.pdf
https://arxiv.org/abs/1807.03403
https://hal.sorbonne-universite.fr/hal-02175769v1/document
https://hal.sorbonne-universite.fr/hal-02175769v1
https://doi.org/10.1016/j.tcs.2019.06.014

Area optimization for MPRM logic circuits based on improved multiple disturbances fireworks algorithm

Authors: Xiang Wang Ke-jian Wang Limin Xiao et al.

Source: Applied Mathematics and Computation. 399:126008

Subject Terms: Switching theory, applications of Boolean algebras to circuits and networks, Applications of mathematical programming, Evolutionary algorithms, genetic algorithms (computational aspects), multiple disturbances, area optimization, fireworks algorithm, 0202 electrical engineering, electronic engineering, information engineering, MPRM logic circuits, 02 engineering and technology, Approximation methods and heuristics in mathematical programming, Latin hypercube sampling

File Description: application/xml

Access URL: https://zbmath.org/7423480
https://doi.org/10.1016/j.amc.2021.126008

Optimization of logistics distribution route through saving algorithm and genetic algorithm

Authors: Liu, Li Chen, Zhifeng Tian, Xue

Subject Terms: Transportation, logistics and supply chain management, saving algorithm, Evolutionary algorithms, genetic algorithms (computational aspects), logistics distribution, genetic algorithm, path optimization, cost control

File Description: application/xml

Access URL: https://zbmath.org/7886199

Fuzzy transportation problem with additional constraint in different environments

Authors: Buvaneshwari, T. K. Anuradha, D.

Subject Terms: Transportation, logistics and supply chain management, centroid method, Evolutionary algorithms, genetic algorithms (computational aspects), genetic algorithm, critical value, transportation problems, Fuzzy and other nonstochastic uncertainty mathematical programming, Approximation methods and heuristics in mathematical programming, type 2 fuzzy variable

File Description: application/xml

Access URL: https://zbmath.org/7614171