Chaos genetic algorithm instead genetic algorithm

Today the Genetic Algorithm (GA) is used to solve a large variety of complex nonlinear optimization problems. However, permute convergence which is one of the most important disadvantages in GA is known to increase the number of iterations for reaching a global optimum. This paper, presents a new GA based on chaotic systems to overcome this shortcoming,. We employ logistic map and tent map as two chaotic systems to generate chaotic values instead of the random values in GA processes. The diversity of the Chaos Genetic Algorithm (CGA) avoids local convergence more often than the traditional GA. Moreover, numerical results show that the proposed method decreases the number of iterations in optimization problems and significantly improves the performance of the basic GA. The idea of utilization of chaotic sequences for optimization algorithms is motivated by biological systems such as Particle Swarm Optimization (PSO), Ant Colony algorithms (ACO) and bee colony algorithms and has the potential to improve ordinary GAs.