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.

References

[1]

E. Cantú-Paz. 1998. A survey of parallel genetic algorithms, Calculateurs Paralleles 10, 2 (1998), 141--171.

Google Scholar

[2]

B. Da, Y. S. Ong, L. Feng, A.K. Qin, A. Gupta, Z. Zhu, C. K. Ting, K. Tang, X. Yao. 2017. Evolutionary multitasking for single-objective continuous optimization: benchmark problems, performance metric, and baseline results. arXiv preprint arXiv:1706.03470 (2017)

Google Scholar

[3]

J. Ding, C. Yang, Y. Jin, T. Chai. 2017. Generalized multi-tasking for evolutionary optimization of expensive problems, IEEE Trans. on Evolutionary Computation (Early Access Paper: Online Available from IEEE Xplore)

Google Scholar

[4]

M. Gorges-Schleuter. 1990. Explicit parallelism of genetic algorithms through population structures. In Proceedings of International Conference of Parallel Problem Solving from Nature I (PPSN 1990), Springer, Dortmund, Germany, 150--159.

Digital Library

Google Scholar

[5]

A. Gupta, Y. S. Ong, and L. Feng. 2016. Multifactorial evolution: Towards evolutionary multitasking. IEEE Trans. on Evolutionary Computation 20, 3(2016), 343--357.

Crossref

Google Scholar

[6]

A. Gupta, Y. S. Ong, L. Feng and K. C. Tan. 2017. Multiobjective multifactorial optimization in evolutionary multitasking. IEEE Trans. on Cybernetics 47, 7(2017), 1652--1665.

Crossref

Google Scholar

[7]

A. Gupta, B. Da, Y. Yuan and Y. S. Ong. 2017. On the emerging notion of evolutionary multitasking: A Computational analog of cognitive multitasking. Recent Advances in Evolutionary Multi-objective Optimization. New York, USA: Springer, 2017, 139--157.

Google Scholar

[8]

A. Gupta, Y. S. Ong, L. Feng. 2015. Evolutionary multitasking in bi-level optimization. Complex & Intelligent Systems 1, 1--4 (2015), 83--95.

Crossref

Google Scholar

[9]

Y. S. Ong, A. Gupta. 2016. Evolutionary multitasking: A computer science view of cognitive multitasking. Cognitive Computation 8, 2 (2016), 521--544.

Crossref

Google Scholar

[10]

L. Pan, C. He, C. He, Y. Tian, H. Wang, X. Zhang, and Y. Jin. 2018. A Classification Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization. IEEE Trans. on Evolutionary Computation (Early Access Paper: Online Available from IEEE Xplore)

Google Scholar

[11]

Y. W, Wen and C. K. Ting. 2017. Parting ways and reallocating resources in evolutionary multitasking. In Proceeding of 2017 Congress on Evolutionary Computation (CEC 2017), IEEE, San Sebastián, Spain, 1295--1302.

Crossref

Google Scholar

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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
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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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Published: 06 July 2018

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GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

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https://dl.acm.org/doi/10.1145/3638529.3653989
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
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An island-based differential evolution algorithm with the multi-size populations

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An island-based differential evolution algorithm with the multi-size populations

An effective new island model genetic algorithm for job shop scheduling problem

An operation to promote diversity in evolutionary algorithms in a dynamic hybrid island model

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