Alan Piszcz

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Recently there has been considerable interest in determining whether, and how much, evolutionary pressure for genetic robustness influences evolutionary processes. In this paper, we attempt to show that this evolutionary pressure does have a significant effect in typical genetic programming problems. Specifically we demonstrate that in a standard genetic(More)
The population size in evolutionary computation is a significant parameter affecting computational effort and the ability to successfully evolve solutions. We find that population size sensitivity - how much a genetic program's efficiency varies with population size - is correlated with problem complexity. An analysis of population sizes was conducted using(More)
We hypothesize that the relationship between parameter settings, specifically parameters controlling mutation, and performance is non-linear in genetic programs. Genetic programming environments have few means for a priori determination of appropriate parameters values. The hypothesized nonlinear behavior of genetic programming creates difficulty in(More)
In this paper we test whether a correlation exists between the optimal mutation rate and problem difficulty. We find that the range of optimal mutation rates is inversely proportional to problem difficulty. We use numerical sweeps of the mutation rate parameter to probe a problem with tunable difficulty. The tests include 3 different types of individual(More)
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