Chaotic Genetic Algorithm and The Effects of Entropy in Performance Optimization
This work addresses performance optimization in genetic algorithms for complex search spaces, but it appears incremental as it builds on existing chaotic genetic algorithm concepts.
The paper tackles the problem of optimizing genetic algorithms by introducing chaotic maps to modify initial population parameters and analyzing entropy, finding a direct relationship between entropy and performance in optimizing nine benchmark functions.
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.