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

Population-based incremental learning with memory scheme for changing environments

Copyright @ 2005 ACMIn recent years there has been a growing interest in studying evolutionary algorithms for dynamic optimization problems due to its importance in real world applications. Several approaches have been developed, such as the memory scheme. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic optimization problems. A PBIL-specific memory scheme is proposed to improve its adaptability in dynamic environments. In this memory scheme the working probability vector is stored together with the best sample it creates in the memory and is used to reactivate old environments when change occurs. Experimental study based on a series of dynamic environments shows the efficiency of the memory scheme for PBILs in dynamic environments. In this paper, the relationship between the memory scheme and the multipopulation scheme for PBILs in dynamic environments is also investigated. The experimental results indicate a negative interaction of the multi-population scheme on the memory scheme for PBILs in the dynamic test environments

Dominance learning in diploid genetic algorithms for dynamic optimization problems

Copyright @ 2006 YangThis paper proposes an adaptive dominance mechanism for diploidy genetic algorithms in dynamic environments. In this scheme, the genotype to phenotype mapping in each gene locus is controlled by a dominance probability, which is learned adaptively during the searching progress and hence is adapted to the dynamic environment. Using a series of dynamic test problems, the proposed dominance scheme is compared to two other dominance schemes for diploidy genetic algorithms. The experimental results validate the efficiency of the proposed dominance learning scheme

PDGA: The primal-dual genetic algorithm

Copyright @ 2003 IOS PressGenetic algorithms (GAs) are a class of search algorithms based on principles of natural evolution. Hence, incorporating mechanisms used in nature may improve the performance of GAs. In this paper inspired by the mechanisms of complementarity and dominance that broadly exist in nature, we present a new genetic algorithm — Primal-Dual Genetic Algorithm (PDGA). PDGA operates on a pair of chromosomes that are primal-dual to each other through the primal-dual mapping, which maps one to the other with a maximum distance away in a given distance space in genotype. The primal-dual mapping improves the exploration capacity of PDGA and thus its searching efficiency in the search space. To test the performance of PDGA, experiments were carried out to compare PDGA over traditional simple GA (SGA) and a peer GA, called Dual Genetic Algorithm (DGA), over a typical set of test problems. The experimental results demonstrate that PDGA outperforms both SGA and DGA on the test set. The results show that PDGA is a good candidate genetic algorithm

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

Statistics-based adaptive non-uniform crossover for genetic algorithms

Copyright @ 2002 University of BirminghamThrough the population, genetic algorithm (GA) implicitly maintains 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, called SANUX, has been proposed. SANUX uses the statistics information of the alleles in each locus to adaptively calculate the swapping probability of that locus for crossover. A simple triangular function has been used to calculate the swapping probability. In this paper two different functions, the trapezoid and exponential functions, are investigated for SANUX insteadd of the triangular function. The experiment results show that both functions further improve the performance of SANUX across a typical set of GA's test problems

A hybrid genetic algorithm and inver over approach for the travelling salesman problem

This article posted here with permission of the IEEE - Copyright @ 2010 IEEEThis paper proposes a two-phase hybrid approach for the travelling salesman problem (TSP). The first phase is based on a sequence based genetic algorithm (SBGA) with an embedded local search scheme. Within the SBGA, a memory is introduced to store good sequences (sub-tours) extracted from previous good solutions and the stored sequences are used to guide the generation of offspring via local search during the evolution of the population. Additionally, we also apply some techniques to adapt the key parameters based on whether the best individual of the population improves or not and maintain the diversity. After SBGA finishes, the hybrid approach enters the second phase, where the inver over (IO) operator, which is a state-of-the-art algorithm for the TSP, is used to further improve the solution quality of the population. Experiments are carried out to investigate the performance of the proposed hybrid approach in comparison with several relevant algorithms on a set of benchmark TSP instances. The experimental results show that the proposed hybrid approach is efficient in finding good quality solutions for the test TSPs.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/E060722/1

Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks

Copyright @ Springer-Verlag Berlin Heidelberg 2010.The static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless mesh network, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem in mobile wireless networks. In this paper, we propose to use multi-population GAs with immigrants scheme to solve the dynamic SP problem in MANETs which is the representative of new generation wireless networks. The experimental results show that the proposed GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.This work was supported by the Engineering and Physical Sciences Research Council(EPSRC) of UK under Grant EP/E060722/1

Flagellar Radial Spoke Protein 2 Is a Calmodulin Binding Protein Required for Motility in \u3cem\u3eChlamydomonas reinhardtii\u3c/em\u3e

Genetic and morphological studies have revealed that the radial spokes regulate ciliary and flagellar bending. Functional and biochemical analysis and the discovery of calmodulin in the radial spokes suggest that the regulatory mechanism involves control of axonemal protein phosphorylation and calcium binding to spoke proteins. To identify potential regulatory proteins in the radial spoke, in-gel kinase assays were performed on isolated axonemes and radial spoke fractions. The results indicated that radial spoke protein 2 (RSP2) can bind ATP and transfer phosphate in vitro. RSP2 was cloned and mapped to the PF24 locus, a gene required for motility. Sequencing revealed that pf24 contains a point mutation converting the first ATG to ATA, resulting in only trace amounts of RSP2 and confirming the RSP2 mapping. Surprisingly, the sequence does not include signature domains for conventional kinases, indicating that RSP2 may not perform as a protein kinase in vivo. However, the predicted RSP2 protein sequence contains Ca2+-dependent calmodulin binding motifs and a GAF domain, a domain found in diverse signaling proteins for binding small ligands including cyclic nucleotides. As predicted from the sequence, recombinant RSP2 binds calmodulin in a calcium-dependent manner. We postulate that RSP2 is a regulatory subunit of the radial spoke involved in localization of calmodulin for control of motility