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Evolutionary many-objective optimization

Authors:

Hisao Ishibuchi,

Hiroyuki SatoAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 614 - 661

https://doi.org/10.1145/3319619.3323377

Published: 13 July 2019 Publication History

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Yang LZhang YCao JLi KWang D(2024)A many-objective evolutionary algorithm based on reference vector guided selection and two diversity and convergence enhancement strategiesApplied Soft Computing10.1016/j.asoc.2024.111369154(111369)Online publication date: Mar-2024
https://doi.org/10.1016/j.asoc.2024.111369
Ruppert JAleksandrova MEngel T(2022)k-Pareto Optimality-Based Sorting with Maximization of Choice and Its Application to Genetic OptimizationAlgorithms10.3390/a1511042015:11(420)Online publication date: 8-Nov-2022
https://doi.org/10.3390/a15110420
Laili YWang YFang YPham DLaili YWang YFang YPham D(2021)Evolutionary Optimisation for Robotic Disassembly Sequence Planning and Line BalancingOptimisation of Robotic Disassembly for Remanufacturing10.1007/978-3-030-81799-2_7(85-110)Online publication date: 18-Aug-2021
https://doi.org/10.1007/978-3-030-81799-2_7
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Evolutionary many-objective optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

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When optimizing an multiobjective optimization problem, the evolution of population can be regarded as a approximation to the Pareto Front (PF). Motivated by this idea, we propose an adaptive region decomposition framework: MOEA/D-AM2M for the degenerated ...

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Published In

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

2161 pages

ISBN:9781450367486

DOI:10.1145/3319619

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 13 July 2019

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SIGEVO

GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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Yang LZhang YCao JLi KWang D(2024)A many-objective evolutionary algorithm based on reference vector guided selection and two diversity and convergence enhancement strategiesApplied Soft Computing10.1016/j.asoc.2024.111369154(111369)Online publication date: Mar-2024
https://doi.org/10.1016/j.asoc.2024.111369
Ruppert JAleksandrova MEngel T(2022)k-Pareto Optimality-Based Sorting with Maximization of Choice and Its Application to Genetic OptimizationAlgorithms10.3390/a1511042015:11(420)Online publication date: 8-Nov-2022
https://doi.org/10.3390/a15110420
Laili YWang YFang YPham DLaili YWang YFang YPham D(2021)Evolutionary Optimisation for Robotic Disassembly Sequence Planning and Line BalancingOptimisation of Robotic Disassembly for Remanufacturing10.1007/978-3-030-81799-2_7(85-110)Online publication date: 18-Aug-2021
https://doi.org/10.1007/978-3-030-81799-2_7
Liu ZZhang FWang XZhao QZhang CZhang B(2021)A Many-Objective Squirrel Hybrid Optimization Algorithm: MaSHOASimulation Tools and Techniques10.1007/978-3-030-72792-5_36(442-459)Online publication date: 27-Apr-2021
https://doi.org/10.1007/978-3-030-72792-5_36
Ohki M(2020)Effectiveness of NSGA-II with Linearly Scheduled Pareto-Partial Dominance for Practical Many-Objecitve Nurse Scheduling2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)10.1109/CoDIT49905.2020.9263847(581-586)Online publication date: 29-Jun-2020
https://doi.org/10.1109/CoDIT49905.2020.9263847
Ohki M(2020)Verification of Applicability of MOEAs to Many-Objective GP Problem2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)10.1109/CoDIT49905.2020.9263818(837-842)Online publication date: 29-Jun-2020
https://doi.org/10.1109/CoDIT49905.2020.9263818

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An improved NSGA-III procedure for evolutionary many-objective optimization

Approximate non-dominated sorting for evolutionary many-objective optimization

An evolutionary many-objective optimisation algorithm with adaptive region decomposition

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