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Estimation of evolvability genetic algorithm and dynamic environments

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Abstract

This article investigates the of applicability of adding evolvability promoting mechanisms to a genetic algorithm to enhance its ability to handle perpetually novel dynamic environments, especially one that has stationary periods allowing the Genetic Algorithm (GA) to converge on a temporary global optimum. We utilize both biological and evolutionary computation (EC) definitions of evolvability to create two measures: one based on the improvements in fitness; the other based on the amount of genotypic change. These two evolvability measures are used alongside fitness to modify how selection proceeds in the GA. We call this modified GA the Estimation of Evolvability Genetic Algorithm (EEGA). When tested against a regular GA (with random immigrants), the EEGA is able to track the global optimum more closely than the GA during the dynamic period. Unlike most GA extensions, the EEGA works effectively at a lower level of diversity than does the GA, showing that it is the quality of the diverse members in the population and not just the quantity that helps the GA evolve.

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Notes

  1. The emphasis on diversity alone, while common, is not universal. Moving beyond the GA world, the idea of targeting diversity and evolving step-sizes is common in Evolutionary Strategies [8].

  2. The building block hypothesis has become quite controversial over the years (see [7] and [22] for example); however, the concepts of building blocks still provide good intuition when thinking about the behavior of the GA.

  3. Premature convergence is said to occur when a population becomes dominated by individuals with relatively poor fitness, but where the population shows no sign of moving to a more highly fit area of the search space.

  4. Based on the dynamic performance of the GA, the relationship between selection effect and innovation effect is not always positive in an evolutionary process, however, the optimal relationship should be positive, which implies good fitness and a healthy gene pool.

  5. Of course Price's equation has been used for other purposes in EC, for example see [4].

  6. Non-parametric statistics were used, e.g. percentiles instead of averages and standard deviations, as the results were not normally distributed.

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Acknowledgments

This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).

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  1. Department of Computing and Information Science, University of Guelph, Guelph, Ontario, Canada, N1G 2W1

    Yao Wang & Mark Wineberg

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Wang, Y., Wineberg, M. Estimation of evolvability genetic algorithm and dynamic environments. Genet Program Evolvable Mach 7, 355–382 (2006). https://doi.org/10.1007/s10710-006-9015-5

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