A Crossover Operator Using Independent Component Analysis for Real-Coded Genetic Algorithms

For Real-coded Genetic Algorithms, there have been proposed many crossover operators. The blend crossover (BLX-α) proposed by Eshelman and Schaffer shows good search ability for separable fitness functions. However, because of its component-wise operation, the BLX-α faces difficulties in optimization of non-separable fitness functions. The present paper proposes a novel crossover operator that combines the BLXα with the Independent Component Analysis (ICA). That is, by applying the ICA to the population, the coordinate system of the search space is transformed so as to increase separability of the fitness function, and then the BLX-α is applied. Computer simulation shows good search ability of the proposed method for non-separable fitness functions.