research-article

Self-adaptive mutation in the differential evolution

Authors:

Rodrigo César Pedrosa Silva,

Rodolfo Ayala Lopes,

Frederico Gadelha GuimarãesAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 1939 - 1946

https://doi.org/10.1145/2001576.2001837

Published: 12 July 2011 Publication History

Abstract

The Differential Evolution (DE) algorithm is an efficient and powerful evolutionary algorithm (EA) for solving optimization problems. However the success of DE in solving a specific problem is closely related to appropriately choosing its control parameters. Parameter tuning leads to additional computational costs because of time-consuming trial-and-error tests. Self-adaptation, in contrast, allows the algorithm to reconfigure itself, automatically adapting to the problem being solved. There are in the literature some self-adaptive versions of differential evolution, however they do not align completely with self-adaptation concepts. In this paper, some self-adaptive versions of DE in the literature are described and discussed, and then a new Self-Adaptive Differential Evolution with multiple mutation strategies is proposed; it is called Self-adaptive Mutation Differential Evolution (SaMDE) and aims at preserving the essential characteristics of self-adaptation. Some computational experiments which illustrate algorithm behaviour and a comparative test with the classical DE and with an important self-adaptive DE are presented. The results suggest that SaMDE is a very promising algorithm.

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https://doi.org/10.3390/math10152675
Gupta SSu R(2022)An efficient differential evolution with fitness-based dynamic mutation strategy and control parametersKnowledge-Based Systems10.1016/j.knosys.2022.109280251(109280)Online publication date: Sep-2022
https://doi.org/10.1016/j.knosys.2022.109280
Mahmud SAbbasi AChakrabortty RRyan M(2022)A self-adaptive hyper-heuristic based multi-objective optimisation approach for integrated supply chain scheduling problemsKnowledge-Based Systems10.1016/j.knosys.2022.109190251(109190)Online publication date: Sep-2022
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Index Terms

Self-adaptive mutation in the differential evolution
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Continuous optimization
        Stochastic control and optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Continuous optimization
        Stochastic control and optimization

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

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Published: 12 July 2011

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GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Ali MP. P. FSalama Abd Elminaam D(2022)A Feature Selection Based on Improved Artificial Hummingbird Algorithm Using Random Opposition-Based Learning for Solving Waste Classification ProblemMathematics10.3390/math1015267510:15(2675)Online publication date: 29-Jul-2022
https://doi.org/10.3390/math10152675
Gupta SSu R(2022)An efficient differential evolution with fitness-based dynamic mutation strategy and control parametersKnowledge-Based Systems10.1016/j.knosys.2022.109280251(109280)Online publication date: Sep-2022
https://doi.org/10.1016/j.knosys.2022.109280
Mahmud SAbbasi AChakrabortty RRyan M(2022)A self-adaptive hyper-heuristic based multi-objective optimisation approach for integrated supply chain scheduling problemsKnowledge-Based Systems10.1016/j.knosys.2022.109190251(109190)Online publication date: Sep-2022
https://doi.org/10.1016/j.knosys.2022.109190
Cao ZWang ZFu YJia HTian F(2022)An adaptive differential evolution framework based on population feature informationInformation Sciences10.1016/j.ins.2022.07.043608(1416-1440)Online publication date: Aug-2022
https://doi.org/10.1016/j.ins.2022.07.043
Iacca Gde Melo V(2022)Cluster-centroid-based mutation strategies for Differential EvolutionSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-021-06448-z26:4(1889-1921)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s00500-021-06448-z
Kong ZWu HJin Z(2022)Dual Acceleration Driven Gray Wolf Optimization Network Coverage AlgorithmProceedings of 2022 10th China Conference on Command and Control10.1007/978-981-19-6052-9_73(812-823)Online publication date: 30-Aug-2022
https://doi.org/10.1007/978-981-19-6052-9_73
Baioletti MDi Bari GMilani APoggioni V(2020)Differential Evolution for Neural Networks OptimizationMathematics10.3390/math80100698:1(69)Online publication date: 2-Jan-2020
https://doi.org/10.3390/math8010069
Zhang XZhan ZZhang J(2020)Adaptive Population Differential Evolution with Dual Control Strategy for Large-Scale Global optimization Problems2020 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC48606.2020.9185854(1-7)Online publication date: Jul-2020
https://doi.org/10.1109/CEC48606.2020.9185854
Deng KChen H(2016)A Hybrid Aerodynamic Optimization Algorithm Based on Differential Evolution and RBF Response Surface17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference10.2514/6.2016-3671Online publication date: 10-Jun-2016
https://doi.org/10.2514/6.2016-3671
Drozdik MAguirre HAkimoto YTanaka K(2015)Comparison of Parameter Control Mechanisms in Multi-objective Differential EvolutionLearning and Intelligent Optimization10.1007/978-3-319-19084-6_8(89-103)Online publication date: 29-May-2015
https://doi.org/10.1007/978-3-319-19084-6_8
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