Genetic algorithm with search area adaptation for the function optimization and its experimental analysis

Abstract

The paper applies a method, Genetic algorithm with Search area Adaptation (GSA), to function optimization. In a previous study (H. Someya and M. Yamamura, 1999), GSA was proposed for the floorplan design problem and it showed better performance than several existing methods. We believe that investigation of the searching behavior of the algorithm is important. However, since the floorplan design problem is a combinatorial optimization problem, we do not know in detail why GSA works well. Thus, we apply GSA to function optimization in order to study the searching behavior in detail. In the function optimization, several benchmarks have been proposed, and their optima and landscapes are known. There is another reason to apply GSA to function optimization: we would like to propose a superior method for function optimization. Through several experiments, we have confirmed that GSA works adaptively and it shows higher performance than existing methods.