(A) genetic algorithm with a mendel operator for multimodal function optimization

Abstract

In this paper, a new genetic algorithm is proposed for solving multimodal function optimization problems that are not easily solved by conventional gentic algorithm(GA)s. This algorithm finds one of local optimizers first and another optimizer at the next iteration. By repeating this process, we can locate all the local solutions instead of one local solution as in conventional GAs. To avoid converging to the same optimizer again, we devise a new genetic operator, called a Mendel operator which simulates the Mendel``s genetic law. The new algorithm using the Mendel operator remembers the optimizers obtained so far, compels individuals to move away from them, and finds a new optimizer.