  This work proposes a new edge about the Chaotic Genetic Algorithm (CGA) and
the importance of the entropy in the initial population. Inspired by chaos
theory the CGA uses chaotic maps to modify the stochastic parameters of Genetic
Algorithm (GA). The algorithm modifies the parameters of the initial population
using chaotic series and then analyzes the entropy of such population. This
strategy exhibits the relationship between entropy and performance optimization
in complex search spaces. Our study includes the optimization of nine benchmark
functions using eight different chaotic maps for each of the benchmark
functions. The numerical experiment demonstrates a direct relation between
entropy and performance of the algorithm.
