A novel hybrid evolutionary programming method for function optimization

The basic evolutionary programming (BEP) method utilizes individual parent fitness to generate offspring. This is objectionable in many optimization problems, where the fitness value grows rapidly with problem dimensions, and two optimization problems differ by simply a scale factor. This paper is concerned with the development of an evolutionary programming method, which is functionally, and structurally equivalent to BEP, but still can be used effectively to optimize functions having strong fitness dependency between parents and their offspring. In this paper, a fitness-blind mutation (FBM) algorithm has been proposed, and then this is used in conjunction with the BEP mutation operator. The FBM operation has been implemented by taking the standard deviation of the Gaussian variable to vary in proportion to the genotypic distance between the individual parent and the fittest individual, which is defined as a pseudo-global optimum individual in a population pool. Also, the directionality of the random variation has been exploited to improve the probability of getting better solutions. In addition to this, the importance of initial search width for generating the offspring has been established empirically. The effectiveness of the proposed algorithm has been verified on well-established test functions.