Conference Proceeding Article
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.
Artificial Intelligence and Robotics | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering
Intelligent Systems and Decision Analytics
Learning and Intelligent Optimization: 10th International Conference, LION 10, Ischia, Italy, May 29 - June 1, 2016, Revised Selected Papers
City or Country
TENG, Teck Hou (DENG Dehao); Stephanus Daniel, Handoko; and LAU, Hoong Chuin.
Self-organizing neural network for adaptive operator selection in evolutionary search. (2016). Learning and Intelligent Optimization: 10th International Conference, LION 10, Ischia, Italy, May 29 - June 1, 2016, Revised Selected Papers. 187-202. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3404
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.