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Author: Evgenii Sopov

Affiliation: Reshetnev Siberian State University of Science and Technology, Russian Federation

ISBN: 978-989-758-274-5

Keyword(s): Genetic Algorithms, Genetic Programming, Constructive Hyper-heuristic, Selection Operator.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Soft Computing

Abstract: Genetic algorithms have proved their efficiency with many hard optimization problems, but in order to achive the best results they must be fine-tuned. One such method of fine-tuning is a synthesis of new genetic operators. Hyper-heuristics represent search techniques that can be used for automating the process of selecting or generating simpler heuristics with the aim of designing new metaheuristic algorithms. In this study, we have proposed a new hyper-heuristic based on genetic programming for the automated synthesis of a selection operator in genetic algorithms. Black-Box Optimization Benchmarking is used as a training set for the genetic programming algorithm and as a test set for estimating the generalization ability of a synthesized selection operator. The results of numerical experiments are presented and discussed. The experiments have shown that the proposed approach can be used for designing new selection operators that outperform standard selection operators on average with new, previously unseen instances of hard black-box optimization problems. (More)

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Paper citation in several formats:
Sopov, E. (2017). Genetic Programming Hyper-heuristic for the Automated Synthesis of Selection Operators in Genetic Algorithms.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 231-238. DOI: 10.5220/0006497002310238

@conference{ijcci17,
author={Evgenii Sopov.},
title={Genetic Programming Hyper-heuristic for the Automated Synthesis of Selection Operators in Genetic Algorithms},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={231-238},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006497002310238},
isbn={978-989-758-274-5},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Genetic Programming Hyper-heuristic for the Automated Synthesis of Selection Operators in Genetic Algorithms
SN - 978-989-758-274-5
AU - Sopov, E.
PY - 2017
SP - 231
EP - 238
DO - 10.5220/0006497002310238

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