Randomness and Chaos in Genetic Algorithms and Differential Evolution

Evolutionary methods and stochastic algorithms in general rely heavily on streams of (pseudo-)random numbers generated in course of their execution. The pseudo-random numbers are utilized for in-silico emulation of probability-driven natural processes such as modification of genetic information (mutation, crossover), partner selection, and survival of the fittest (selection, migration). Deterministic chaos is a very well known mathematical concept that can be used to generate sequences of real numbers within selected interval. In the past, it has been used as a basis for various pseudo-random number generators with interesting properties. This work provides an empirical comparison of the performance of genetic algorithms and differential evolution using different pseudo-random number generators and chaotic systems as sources of stochasticity.