A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

A genetic algorithm with characteristic amplification through multiple geographically isolated populations and varied fitness landscapes

This paper proposes a new approach, wherein multiple populations are evolved on different landscapes. The problem statement is broken down, to describe discrete characteristics. Each landscape, described by its fitness landscape is used to optimize or amplify a certain characteristic or set of characteristics. Individuals from each of these populations are kept geographically isolated from each other Each population is evolved individually. After a predetermined number of evolutions, the system of populations is analysed against a normalized fitness function. Depending on this score and a predefined merging scheme, the populations are merged, one at a time, while continuing evolution. Merging continues until only one final population remains. This population is then evolved, following which the resulting population will contain the optimal solution. The final resulting population will contain individuals which have been optimized against all characteristics as desired by the problem statement. Each individual population is optimized for a local maxima. Thus when populations are merged, the effect is to produce a new population which is closer to the global maxima.

Genetic analysis of Indian tasar silkmoth (Antheraea mylitta) populations

Indian tasar silkmoth, Antheraea mylitta is an economically important wild silkmoth species distributed across India. A number of morphologically and ethologically well-defined ecotypes are known for this species that differ in their primary food plant specificity. Most of these ecotypes do not interbreed in nature, but are able to produce offspring under captive conditions. Microsatellite markers were developed for A. mylitta, and out of these, ten well-behaved microsatellite loci were used to analyze the population structure of different ecoraces. A total of 154 individual moths belonging to eight different ecoraces, were screened at each locus. Hierarchical analysis of population structure using Analysis of MOlecular VAriance (AMOVA) revealed significant structuring (F-ST = 0.154) and considerable inbreeding (F-IS = 0.505). A significant isolation by distance was also observed. The number of possible population clusters was investigated using distance method, Bayesian algorithm and self organization maps (SOM). The first two methods revealed two distinct clusters, whereas the SOM showed the different ecoraces not to be clearly differentiated. These results suggest that although there is a large degree of phenotypic variation among the different ecoraces of A. mylitta, genetically they are not very different, and the phenotypic differences may largely be a result of their respective ecology.

Inbreeding and the incidence of childhood genetic disorders in Karnataka, South India

Consanguineous marriages are strongly favoured among the populations of South India. In a study conducted on 407 infants and children, a total of 35 genetic diseases was diagnosed in 63 persons: 44 with single gene defects, 12 with polygenic disorders, and seven with Down's syndrome. The coefficient of inbreeding of the total study group, F = 0.0414, was significantly higher than that previously calculated for the general population, F = 0.0271, and autosomal recessive disorders formed the largest single disease category diagnosed. The results suggest that long term inbreeding may not have resulted in appreciable elimination of recessive lethals and sub-lethals from the gene pool.

On evolutionarily stable compositions of populations of interacting genotypes.

Consider an organism in which the genetic fitness of an individual depends to a large extent on its social interactions. Assuming the genotypes to differ only in the choice of strategies they adopt in social interactions, and equating the variation in genetic fitness to the mean payoff to an individual averaged over all possible encounters, we develop a dynamical model for the evolution of genotypic frequencies in such a population. Such a system is characterised by frequency dependent selection, and depending on the initial composition, the population evolves towards one of several possible compositions. We term as evolutionarily stable compositions (ESC) any such composition towards which a population can evolve and which is stable against small fluctuations in the frequencies of existing genotypes as well as to invasions by any other postulated genotype. We state the necessary and sufficient conditions for the identification of all possible ESC's for any number of interacting genotypes. Our results conform to those derived earlier in connection with the concept of evolutionarily stable strategies only in the case of two interacting genotypes; when more than two genotypes interact the conditions under which various ESC's exist become far richer. We consider interactions with mixed strategists and show that in a conflict with pure strategists the optimal mixed strategist will be the only one to ultimately survive. We illustrate our approach by considering the specific case of a primitively social wasp.

trans gene regulation in adaptive evolution: A genetic algorithm model

This is a continuation of earlier studies on the evolution of infinite populations of haploid genotypes within a genetic algorithm framework. We had previously explored the evolutionary consequences of the existence of indeterminate—“plastic”—loci, where a plastic locus had a finite probability in each generation of functioning (being switched “on”) or not functioning (being switched “off”). The relative probabilities of the two outcomes were assigned on a stochastic basis. The present paper examines what happens when the transition probabilities are biased by the presence of regulatory genes. We find that under certain conditions regulatory genes can improve the adaptation of the population and speed up the rate of evolution (on occasion at the cost of lowering the degree of adaptation). Also, the existence of regulatory loci potentiates selection in favour of plasticity. There is a synergistic effect of regulatory genes on plastic alleles: the frequency of such alleles increases when regulatory loci are present. Thus, phenotypic selection alone can be a potentiating factor in a favour of better adaptation.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

A Dynamic Migration Model for Self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

A Dynamic Migration Model for self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

Evolution of polyandry by reduction in progeny number variance in structured populations

When there is a variation in the quality of males in a population, multiple mating can lead to an increase in the genetic fitness of a female by reducing the variance of the progeny number. The extent of selective advantage obtainable by this process is investigated for a population subdivided into structured demes. It is seen that for a wide range of model parameters (deme size, distribution of male quality, local resource level), multiple mating leads to a considerable increase in the fitness. Frequency-dependent selection or a stable coexistence between polyandry and monandry can also result when the possible costs involved in multiple mating are taken into account.

On an exact populationary model of genetic algorithms

Genetic algorithms (GAs) are search methods that are being employed in a multitude of applications with extremely large search spaces. Recently, there has been considerable interest among GA researchers in understanding and formalizing the working of GAs. In an earlier paper, we have introduced the notion of binomially distributed populations as the central idea behind an exact ''populationary'' model of the large-population dynamics of the GA operators for objective functions called ''functions of unitation.'' In this paper, we extend this populationary model of GA dynamics to a more general class of objective functions called functions of unitation variables. We generalize the notion of a binomially distributed population to a generalized binomially distributed population (GBDP). We show that the effects of selection, crossover, and mutation can be exactly modelled after decomposing the population into GBDPs. Based on this generalized model, we have implemented a GA simulator for functions of two unitation variables-GASIM 2, and the distributions predicted by GASIM 2 match with those obtained from actual GA runs. The generalized populationary model of GA dynamics not only presents a novel and natural way of interpreting the workings of GAs with large populations, but it also provides for an efficient implementation of the model as a GA simulator. (C) Elsevier Science Inc. 1997.

Differences in host species relationships and biogeographic influences produce contrasting patterns of prevalence, community composition and genetic structure in two genera of avian malaria parasites in southern Melanesia

1. Host-parasite interactions have the potential to influence broadscale ecological and evolutionary processes, levels of endemism, divergence patterns and distributions in host populations. Understanding the mechanisms involved requires identification of the factors that shape parasite distribution and prevalence. 2. A lack of comparative information on community-level host-parasite associations limits our understanding of the role of parasites in host population divergence processes. Avian malaria (haemosporidian) parasites in bird communities offer a tractable model system to examine the potential for pathogens to influence evolutionary processes in natural host populations. 3. Using cytochrome b variation, we characterized phylogenetic diversity and prevalence of two genera of avian haemosporidian parasites, Plasmodium and Haemoproteus, and analysed biogeographic patterns of lineages across islands and avian hosts, in southern Melanesian bird communities to identify factors that explain patterns of infection. 4. Plasmodium spp. displayed isolation-by-distance effects, a significant amount of genetic variation distributed among islands but insignificant amounts among host species and families, and strong local island effects with respect to prevalence. Haemoproteus spp. did not display isolation-by-distance patterns, showed marked structuring of genetic variation among avian host species and families, and significant host species prevalence patterns. 5. These differences suggest that Plasmodium spp. infection patterns were shaped by geography and the abiotic environment, whereas Haemoproteus spp. infection patterns were shaped predominantly by host associations. Heterogeneity in the complement and prevalence of parasite lineages infecting local bird communities likely exposes host species to a mosaic of spatially divergent disease selection pressures across their naturally fragmented distributions in southern Melanesia. Host associations for Haemoproteus spp. indicate a capacity for the formation of locally co-adapted host-parasite relationships, a feature that may limit intraspecific gene flow or range expansions of closely related host species.