Gradient based stochastic mutation operators in evolutionary multi-objective optimization

Pradyumn Kumar Shukla

doi:10.1007/978-3-540-71618-1_7

Gradient based stochastic mutation operators in evolutionary multi-objective optimization

Pradyumn Kumar Shukla^*

^*Corresponding author for this work

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Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Evolutionary algorithms have been adequately applied in solving single and multi-objective optimization problems. In the single-objective case various studies have shown the usefulness of combining gradient based classical search principles with evolutionary algorithms. However there seems to be a dearth of such studies for the multi-objective case. In this paper, we take two classical search operators and discuss their use as a local search operator in a state-of-the-art evolutionary algorithm. These operators require gradient information which is obtained using a stochastic perturbation technique requiring only two function evaluations. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of hybrid algorithms in solving a large class of complex multi-objective optimization problems.

Original language	English
Title of host publication	Adaptive and Natural Computing Algorithms - 8th International Conference, ICANNGA 2007, Proceedings
Publisher	Springer-Verlag Italia
Pages	58-66
Number of pages	9
Edition	PART 1
ISBN (Print)	9783540715894
DOIs	https://doi.org/10.1007/978-3-540-71618-1_7
Publication status	Published - 2007
Event	8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007 - Warsaw, Poland Duration: 11 Apr 2007 → 14 Apr 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	4431 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007
Country/Territory	Poland
City	Warsaw
Period	11/04/07 → 14/04/07

Keywords

Mutation Operator
Search Operator
Binary Indicator
Local Search Operator
Simultaneous Perturbation

Access to Document

10.1007/978-3-540-71618-1_7

Cite this

Shukla, P. K. (2007). Gradient based stochastic mutation operators in evolutionary multi-objective optimization. In Adaptive and Natural Computing Algorithms - 8th International Conference, ICANNGA 2007, Proceedings (PART 1 ed., pp. 58-66). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4431 LNCS, No. PART 1). Springer-Verlag Italia. https://doi.org/10.1007/978-3-540-71618-1_7

Shukla, Pradyumn Kumar. / Gradient based stochastic mutation operators in evolutionary multi-objective optimization. Adaptive and Natural Computing Algorithms - 8th International Conference, ICANNGA 2007, Proceedings. PART 1. ed. Springer-Verlag Italia, 2007. pp. 58-66 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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abstract = "Evolutionary algorithms have been adequately applied in solving single and multi-objective optimization problems. In the single-objective case various studies have shown the usefulness of combining gradient based classical search principles with evolutionary algorithms. However there seems to be a dearth of such studies for the multi-objective case. In this paper, we take two classical search operators and discuss their use as a local search operator in a state-of-the-art evolutionary algorithm. These operators require gradient information which is obtained using a stochastic perturbation technique requiring only two function evaluations. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of hybrid algorithms in solving a large class of complex multi-objective optimization problems.",

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note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007 ; Conference date: 11-04-2007 Through 14-04-2007",

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Shukla, PK 2007, Gradient based stochastic mutation operators in evolutionary multi-objective optimization. in Adaptive and Natural Computing Algorithms - 8th International Conference, ICANNGA 2007, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 4431 LNCS, Springer-Verlag Italia, pp. 58-66, 8th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2007, Warsaw, Poland, 11/04/07. https://doi.org/10.1007/978-3-540-71618-1_7

Gradient based stochastic mutation operators in evolutionary multi-objective optimization. / Shukla, Pradyumn Kumar.
Adaptive and Natural Computing Algorithms - 8th International Conference, ICANNGA 2007, Proceedings. PART 1. ed. Springer-Verlag Italia, 2007. p. 58-66 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4431 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Evolutionary algorithms have been adequately applied in solving single and multi-objective optimization problems. In the single-objective case various studies have shown the usefulness of combining gradient based classical search principles with evolutionary algorithms. However there seems to be a dearth of such studies for the multi-objective case. In this paper, we take two classical search operators and discuss their use as a local search operator in a state-of-the-art evolutionary algorithm. These operators require gradient information which is obtained using a stochastic perturbation technique requiring only two function evaluations. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of hybrid algorithms in solving a large class of complex multi-objective optimization problems.

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KW - Search Operator

KW - Binary Indicator

KW - Local Search Operator

KW - Simultaneous Perturbation

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Shukla PK. Gradient based stochastic mutation operators in evolutionary multi-objective optimization. In Adaptive and Natural Computing Algorithms - 8th International Conference, ICANNGA 2007, Proceedings. PART 1 ed. Springer-Verlag Italia. 2007. p. 58-66. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-71618-1_7

Gradient based stochastic mutation operators in evolutionary multi-objective optimization

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