technical-note

A memetic algorithm using local search chaining for black-box optimization benchmarking 2009 for noisy functions

Authors:

Francisco HerreraAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

Pages 2359 - 2366

https://doi.org/10.1145/1570256.1570329

Published: 08 July 2009 Publication History

Abstract

Memetic algorithms with continuous local search methods have arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous search algorithms that stand out as brilliant local search optimisers. Several of them, like CMA-ES, often require a high number of evaluations to adapt its parameters. Unfortunately, this feature makes difficult to use them to create memetic algorithms.

In this work, we show a memetic algorithm that applies CMA-ES to refine the solutions, assigning to each individual a local search intensity that depends on its features, by chaining different local search applications.

Experiments are carried out on the noisy Black-Box Optimization Benchmarking BBOB'2009 test suite.

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Cited By

Llanza AShvai NNakib A(2023)Investigating Fractal Decomposition Based Algorithm on Low-Dimensional Continuous Optimization ProblemsMetaheuristics10.1007/978-3-031-26504-4_16(215-229)Online publication date: 23-Feb-2023
https://doi.org/10.1007/978-3-031-26504-4_16
Bansal RSrivastava K(2018)A memetic algorithm for the cyclic antibandwidth maximization problemSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-009-0538-615:2(397-412)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1007/s00500-009-0538-6

Index Terms

A memetic algorithm using local search chaining for black-box optimization benchmarking 2009 for noisy functions
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
    2. Numerical analysis
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers

July 2009

1760 pages

ISBN:9781605585055

DOI:10.1145/1570256

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 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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Published: 08 July 2009

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July 8 - 12, 2009

Québec, Montreal, Canada

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Cited By

Llanza AShvai NNakib A(2023)Investigating Fractal Decomposition Based Algorithm on Low-Dimensional Continuous Optimization ProblemsMetaheuristics10.1007/978-3-031-26504-4_16(215-229)Online publication date: 23-Feb-2023
https://doi.org/10.1007/978-3-031-26504-4_16
Bansal RSrivastava K(2018)A memetic algorithm for the cyclic antibandwidth maximization problemSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-009-0538-615:2(397-412)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1007/s00500-009-0538-6

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