A Biologically-Inspired Model for Mass Extinction in Genetic Algorithms

Mass extinction events have previously been shown to be a catalyst for accelerating the rate of evolution in genetic algorithms. This increased evolution rate combined with a destabilization of the dynamic equilibrium of the genetic algorithm can allow the algorithm to overcome local maxima in the solution space; however, most implementations of mass extinction squander the full benefit of the increased evolutionary rate by relying upon random chance to generate better solutions in the post-extinction generations. This paper examines the hypothesis that, by increasing variability and decreasing selection pressure immediately after an extinction event, the algorithm has a higher chance of yielding more fit individuals and overcoming local maxima in the post-extinction generations. We compare the performance of a genetic algorithm without extinction with the two approaches described above on a problem with a large search space containing many local optima, point scattering in the unit circle, and examine the effects of extinction methods on performance. The preliminary results presented here show that decreasing selection pressure and increasing variability help the genetic algorithm achieve higher success rates than with standard mass extinction.