research-article

Analysis of evolutionary multi-tasking as an island model

Authors:

Ryuichi Hashimoto,

Hisao Ishibuchi,

Naoki Masuyama,

Yusuke NojimaAuthors Info & Claims

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1894 - 1897

https://doi.org/10.1145/3205651.3208228

Published: 06 July 2018 Publication History

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Abstract

Recently, an idea of evolutionary multi-tasking has been proposed and applied to various types of optimization problems. The basic idea of evolutionary multi-tasking is to simultaneously solve multiple optimization problems (i.e., tasks) in a cooperative manner by a single run of an evolutionary algorithm. For this purpose, each individual in a population has its own task. This means that a population of individuals can be viewed as being divided into multiple sub-populations. The number of sub-populations is the same as the number of tasks to be solved. In this paper, first we explain that a multi-factorial evolutionary algorithm (MFEA), which is a representative algorithm of evolutionary multi-tasking, can be viewed as a special island model. MFEA has the following two features: (i) Crossover is performed not only within an island but also between islands, and (ii) no migration is performed between islands. Information of individuals in one island is transferred to another island through inter-island crossover. Next, we propose a simple implementation of evolutionary multi-tasking in the framework of the standard island model. Then, we compare our island model with MFEA through computational experiments. Promising results are obtained by our implementation of evolutionary multi-tasking.

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Huang YZhou WWang YLi MFeng LTan K(2024)Evolutionary Multitasking With Centralized Learning for Large-Scale Combinatorial Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.332387728:5(1499-1513)Online publication date: Oct-2024
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Published In

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2018

1968 pages

ISBN:9781450357647

DOI:10.1145/3205651

Editor:
Hernan Aguirre
Shinshu University
,
General Chair:
Keiki Takadama
The University of Electro-Communications

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2018

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GECCO '18

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SIGEVO

GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

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

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Li YGong WGu QLi XHandl J(2024)Transfer Search Directions Among Decomposed Subtasks for Evolutionary Multitasking in Multiobjective OptimizationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3653989(557-565)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3653989
Huang YZhou WWang YLi MFeng LTan K(2024)Evolutionary Multitasking With Centralized Learning for Large-Scale Combinatorial Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.332387728:5(1499-1513)Online publication date: Oct-2024
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Liaw RWen Y(2024)Ensemble Learning Through Evolutionary Multitasking: A Formulation and Case StudyIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33699498:4(3081-3094)Online publication date: Aug-2024
https://doi.org/10.1109/TETCI.2024.3369949
Zhang TGong WLi Y(2024)Multitask differential evolution with adaptive dual knowledge transferApplied Soft Computing10.1016/j.asoc.2024.112040165(112040)Online publication date: Nov-2024
https://doi.org/10.1016/j.asoc.2024.112040
da Silveira Lde Lima TBarros JSoncco-Álvarez JLlanos CAyala-Rincón M(2024)On the behavior of parallel island modelsApplied Soft Computing10.1016/j.asoc.2023.110880148:COnline publication date: 27-Feb-2024
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Qiao KYu KQu BLiang JSong HYue CLin HTan K(2023)Dynamic Auxiliary Task-Based Evolutionary Multitasking for Constrained Multiobjective OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2022.317506527:3(642-656)Online publication date: Jun-2023
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