Adaptive non-uniform mutation based on statistics for genetic algorithms

Copyright @ 2002 ACMAs a meta-heuristic search algorithm based on mechanisms abstracted from population genetics, the genetic algorithm (GA) implicitly maintains the statistics about the search space through the population. This implicit statistics can be explicitly used to enhance GA's performance. In this paper, a statistics-based adaptive non-uniform mutation (SANUM) is proposed. SANUM uses the statistics information of the allele distribution in each locus to adatively adjust the mutation operation. Our preliminary experiments show that SANUM outperforms traditional bit flip mutation across a representative set set of test problems

Statistics-based adaptive non-uniform mutation for genetic algorithms

This is the post-print version of the article - Copyright @ 2003 Springer-VerlagA statistics-based adaptive non-uniform mutation (SANUM) is presented for genetic algorithms (GAs), within which the probability that each gene will subject to mutation is learnt adaptively over time and over the loci. SANUM uses the statistics of the allele distribution in each locus to adaptively adjust the mutation probability of that locus. The experiment results demonstrate that SANUM performs persistently well over a range of typical test problems while the performance of traditional mutation operators with fixed rates greatly depends on the problems. SANUM represents a robust adaptive mutation that needs no advanced knowledge about the problem landscape

Adaptive group mutation for tackling deception in genetic search

Copyright @ 2004 WSEASIn order to study the efficacy of genetic algorithms (GAs), a number of fitness landscapes have been designed and used as test functions. Among these functions a family of deceptive functions have been developed as difficult test functions for comparing different implementations of GAs. In this paper an adaptive group mutation (AGM), which can be combined with traditional bit mutation in GAs, is proposed to tackle the deception problem in genetic searching. Within the AGM, those genes that have converged to certain threshold degree are adaptively grouped together and subject to mutation together with a given probability. To test the performance of the AGM, experiments were carried out to compare GAs that combine the AGM and GAs that use only traditional bit mutation with a number of suggested “standard” fixed mutation rates over a set of deceptive functions as well as non-deceptive functions. The results demonstrate that GAs with the AGM perform better than GAs with only traditional bit mutation over deceptive functions and as well as GAs with only traditional bit mutation over non-deceptive functions. The results show that the AGM is a good choice for GAs since most problems may involve some degree of deception and deceptive functions are difficult for GAs

A multi-agent based evolutionary algorithm in non-stationary environments

This article is posted here with permission of IEEE - Copyright @ 2008 IEEEIn this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolve to find optimum. All agents live in a lattice like environment, where each agent is fixed on a lattice point. In order to increase the energy, agents can compete with their neighbors and can also acquire knowledge based on statistic information. In order to maintain the diversity of the population, the random immigrants and adaptive primal dual mapping schemes are used. Simulation experiments on a set of dynamic benchmark problems show that MAEA can obtain a better performance in non-stationary environments in comparison with several peer genetic algorithms.This work was suported by the Key Program of National Natural Science Foundation of China under Grant No. 70431003, the Science Fund for Creative Research Group of the National Natural Science Foundation of China under Grant No. 60521003, the National Science and Technology Support Plan of China under Grant No. 2006BAH02A09, and the Engineering and Physical Sciences Research Council of the United Kingdom under Grant No. EP/E060722/1

Adaptive crossover in genetic algorithms using statistics mechanism

The final published version of this article is available at the link below. Copyright @ MIT Press.Genetic Algorithms (GAs) emulate the natural evolution process and maintain a popilation of potential solutions to a given problem. Through the population, GAs implicitly maintain the statistics about the search space. This implicit statistics can be used explicitly to enhance GA's performance. Inspired by this idea, a statistics-based adaptive non-uniform crossover (SANUX) has been proposed. SANUX uses the statisics information of the alleles in each locus to adaptively caluclate the swapping probability of that locus for crossover operation. A simple triangular function has been used to calculate the swapping probability. In this paper new functions, the trapezoid and exponential functions, are proposed for SANUX instead of the triangular function. Experiment results show that both functions further improve the performance of SANUX