Self-adaptive mutation only genetic algorithm: An application on the optimization of airport capacity utilization

Abstract

A new type of adaptive evolutionary algorithm that combines two genetic algorithms using mutation matrix is developed based on an adaptive resource allocation of CPU time. Performance evaluations are made on the airport scheduling problem with constraint. The two genetic algorithms used are based on the construction of the mutation matrix M(t), which is problem independent as it uses the fitness distribution in the population and the statistical information of the locus only. The mutation matrix is parameter free and adaptive since the matrix elements are time dependent and inherits the information accumulated from past generations. A self-adaptive time sharing method is introduced to allocate resource to the two different strategies, which uses the theory of mean-variance analysis in portfolio management. The application to airport scheduling demonstrates that the self-adaptive mutation only genetic algorithm is able to provide quality solutions efficiently. © 2008 Springer Berlin Heidelberg.