Abstract
Stochastic nature-inspired optimization and search methods depend on streams of integer and floating point 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) and environmental effects (small random changes in motion direction and velocity). Deterministic chaos is a well known mathematical concept that can be used to generate sequences of seemingly random real numbers within selected interval in a predictable and controllable way. 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, differential evolution, and particle swarm optimization using different pseudo-random number generators and chaotic systems as sources of stochasticity.
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