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MEMPSODE: an empirical assessment of local search algorithm impact on a memetic algorithm using noiseless testbed

Authors:

Grigoris S. Piperagkas,

Konstantinos E. Parsopoulos,

Dimitris G. Papageorgiou,

Isaac E. LagarisAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

Pages 245 - 252

https://doi.org/10.1145/2330784.2330820

Published: 07 July 2012 Publication History

Abstract

Memetic algorithms are hybrid schemes that usually integrate metaheuristics with classical local search techniques, in order to attain more balanced intensification/diversification trade--off in the search procedure. MEMPSODE is a recently published software that implements such memetic schemes, based on the Particle Swarm Optimization and Differential Evolution algorithms, as well as on the Merlin optimization environment that offers a variety of local search methods. The present study aims at investigating the impact of the selected local search algorithm in the memetic schemes produced by MEMPSODE. Our interest was focused on gradient--free local search methods. We applied the derived memetic schemes on the noiseless testbed of the Black--Box Optimization Benchmarking 2012 workshop. The obtained results can offer significant insight to optimization practitioners with respect to the most promising approaches.

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Cited By

Vermetten DWang HBäck TDoerr CCoello Coello C(2020)Towards dynamic algorithm selection for numerical black-box optimizationProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3390189(654-662)Online publication date: 25-Jun-2020
https://dl.acm.org/doi/10.1145/3377930.3390189
Talbi HDraa A(2019)A Continuous Optimization Scheme Based on an Enhanced Differential Evolution and a Trust Region MethodProceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.110.1007/978-3-030-21005-2_22(222-233)Online publication date: 11-Jul-2019
https://doi.org/10.1007/978-3-030-21005-2_22
Voglis CAlba E(2013)Adapt-MEMPSODEProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2466804(1137-1144)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2466804

Index Terms

MEMPSODE: an empirical assessment of local search algorithm impact on a memetic algorithm using noiseless testbed
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
  2. Mathematical software
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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cover image ACM Conferences

GECCO '12: Proceedings of the 14th annual conference companion on Genetic and evolutionary computation

July 2012

1586 pages

ISBN:9781450311786

DOI:10.1145/2330784

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Copyright © 2012 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

New York, NY, United States

Publication History

Published: 07 July 2012

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GECCO '12

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SIGEVO

GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Cited By

Vermetten DWang HBäck TDoerr CCoello Coello C(2020)Towards dynamic algorithm selection for numerical black-box optimizationProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3390189(654-662)Online publication date: 25-Jun-2020
https://dl.acm.org/doi/10.1145/3377930.3390189
Talbi HDraa A(2019)A Continuous Optimization Scheme Based on an Enhanced Differential Evolution and a Trust Region MethodProceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.110.1007/978-3-030-21005-2_22(222-233)Online publication date: 11-Jul-2019
https://doi.org/10.1007/978-3-030-21005-2_22
Voglis CAlba E(2013)Adapt-MEMPSODEProceedings of the 15th annual conference companion on Genetic and evolutionary computation10.1145/2464576.2466804(1137-1144)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2464576.2466804

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