Self-organizing neural network for adaptive operator selection in evolutionary search

• Main Authors: TENG, Teck Hou, HANDOKO, Stephanus Daniel, LAU, Hoong Chuin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: Benchmarking; Combinatorial optimization; Evolutionary algorithms; Markov processes; Neural networks; Optimization; Artificial Intelligence and Robotics; Computer Sciences; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/3404; https://ink.library.smu.edu.sg/context/sis_research/article/4405/viewcontent/SelfOrganzingNNAdaptiveOp_2016_LION.pdf
• Institution: Singapore Management University

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.