Publication Type

Conference Proceeding Article

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.

Discipline

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

Research Areas

Intelligent Systems and Decision Analytics

Publication

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

First Page

187

Last Page

202

ISBN

9783319503486

Identifier

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

Publisher

Springer Verlag

City or Country

Cham

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

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