Optimization of Instance-based Policy Based on Real-coded Genetic Algorithms

SummaryThe purpose of this paper is to propose a new real-coded genetic algorithm (RCGA) named Networked Genetic Algorithm (NGA) that intends to find multiple optima simultaneously in deceptive globally multimodal landscapes. Most current techniques such as niching for finding multiple optima take into account big valley landscapes or nondeceptive globally multimodal landscapes but not deceptive ones called UV-landscapes. Adaptive Neighboring Search (ANS) is a promising approach for finding multiple optima in UV-landscapes. ANS utilizes a restricted mating scheme with a crossover-like mutation in order to find optima in deceptive globally multimodal landscapes. However, ANS has a fundamental problem that it does not find all the optima simultaneously in many cases. NGA overcomes the problem by an adaptive parent-selection scheme and an improved crossover-like mutation. We show the effectiveness of NGA over ANS in terms of the number of detected optima in a single run on Fletcher and Powell functions as benchmark problems that are known to have multiple optima, ill-scaledness, and UV-landscapes.

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Optimization of Instance-based Policy Based on Real-coded Genetic Algorithms

Cited by 2 publications

References 6 publications

A new real-coded genetic algorithm using the adaptive selection network for detecting multiple optima

A new real-coded genetic algorithm using the adaptive selection network for detecting multiple optima

Ｕｖ構造を考慮した適応的複製選択による実数値ｇａの提案

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