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Fitness-Dependent Hybridization of Clonal Selection Algorithm and Random Local Search

Authors:

Nina Bulanova,

Arina Buzdalova,

Maxim BuzdalovAuthors Info & Claims

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

Pages 5 - 6

https://doi.org/10.1145/2908961.2908996

Published: 20 July 2016 Publication History

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Abstract

Artificial immune systems (AIS) and local search algorithms have remarkable differences in the structure of mutation operators. Thus AIS algorithms may be more efficient at the beginning of optimization, while local search algorithms are more efficient in the end, when we need to do small improvements. Our goal is to combine several mutation operators in one algorithm so that the new algorithm will be efficient on fixed budget and will reach optimum within reasonable time bounds.

We propose to select mutation operators used in AIS and local search according to a specific exponential probability function which depends on the fitness of the current individual. During the experimental study, we constructed hybrids from AIS mutation operator CLONALG (Clonal Selection Algorithm) and RLS mutation operator (Random Local Search) and used them to solve OneMax problem. We compared the proposed method with a simple hybrid algorithm and empirically confirmed the hypothesis that hybrids are efficient on fixed budget and need only a slightly higher number of iterations to reach the optimum.

References

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Index Terms

Fitness-Dependent Hybridization of Clonal Selection Algorithm and Random Local Search
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches

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Published In

GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion

July 2016

1510 pages

ISBN:9781450343237

DOI:10.1145/2908961

Editor:
Tobias Friedrich
Hasso Plattner Institute
,
General Chair:
Frank Neumann
University of Adelaide
,
Program Chair:
Andrew M. Sutton
Hasso Plattner Institute

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 20 July 2016

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Ministry of Education and Science of the Russian Federation

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GECCO '16

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SIGEVO

GECCO '16: Genetic and Evolutionary Computation Conference

July 20 - 24, 2016

Colorado, Denver, USA

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GECCO '16 Companion Paper Acceptance Rate 137 of 381 submissions, 36%;

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

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Recommendations

Baldwinian learning in clonal selection algorithm for optimization

A review of clonal selection algorithm and its applications

Hybrid learning clonal selection algorithm

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