Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2016

Abstract

Evolutionary Algorithm is a well-known meta-heuristics paradigm capable of providing high-quality solutions to computationally hard problems. As with the other meta-heuristics, its performance is often attributed to appropriate design choices such as the choice of crossover operators and some other parameters. In this chapter, we propose a continuous state Markov Decision Process model to select crossover operators based on the states during evolutionary search. We propose to find the operator selection policy efficiently using a self-organizing neural network, which is trained offline using randomly selected training samples. The trained neural network is then verified on test instances not used for generating the training samples. We evaluate the efficacy and robustness of our proposed approach with benchmark instances of Quadratic Assignment Problem.

Keywords

Benchmarking, Combinatorial optimization, Evolutionary algorithms, Markov processes, Neural networks, Optimization

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Learning and Intelligent Optimization: 10th International Conference, LION 2016, Ischia, Italy, May 29 - June 1

First Page

187

Last Page

202

ISBN

9783319503486

Identifier

10.1007/978-3-319-50349-3_13

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-319-50349-3_13

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