Abstract
Biologically inspired Evolution Algorithms (EAs), that use individuals as searching points and progress search by evolutions or adaptations of the individuals, are widely applied to many optimization problems. Many real world problems, which could be transformed to optimization problems, are very often difficult because the problems have complex landscapes that are multimodal, epistatic and having strong local minima. Current real-coded genetic algorithms (GAs) could solve high-dimensional multimodal functions, but could not solve strong deceptive functions. Niching GAs are applied to low-dimensional multimodal functions by maintaining diversity of searching population, but could not be applicable to highdimensional functions. In order to optimize high dimensional deceptive multimodal functions, we propose a new EA called Adaptive Neighboring Search (ANS), that is structured with a selection for reproduction by restricting mating individuals to neighbors, a crossover-like mutation (XLM) using the mating individuals and an elitist selection for survival within one centered parent and its offsprings. By individualized generation alternation and complementary crossover-like mutation, the ANS realizes self-distributive and locally adaptive search, and individuals in the search divide into plural promising valleys and converge within same valley. The ANS is applicable to high dimensional deceptive multimodal function optimization, because the feature is independent of number of problem’s dimensions. By applying to high dimensional Fletcher and Powell function as a deceptive multimodal one, we show the ANS can obtain various solutions and several optimal solutions in high probability.
