Improved genetic algorithm using chaotic cellular automata — CCAGA

Genetic Algorithm (GA) is a search technique used to find the optimized solution in a problem space. The main problem in using the GA is in complex multi-peak search problems that usually leads to premature convergence. Furthermore certain optimization problems, such as variant problems, cannot be solved by means of genetic algorithms. This occurs due to poorly known fitness functions which generate bad chromosome blocks in spite of the fact that only good chromosome blocks crossover. In this paper by using Chaotic Cellular Automata (CCA) along with influencing Pseudo Random Number Generator (PRN), a new and enhanced method for GA is presented. Mutation, crossover and elitism's percentage selection are all influenced by Pseudo Random Number Generator (PRNG), and consequently, chaotic numbers are produced which completely change the GA performance. Mentioned factors lead to the appropriate random behavior for the genome in the problem space which give the GA, high exploitation and exploration ability. Moreover this unpredictable behavior in changing the GA's factors with the percentage of elitism selection created by CCA, help the proposed algorithm to avoid converging prematurely and falling in local minimums as well as the ability to cover the bigger space's problem. In comparison with traditional GA algorithm, the proposed method illustrates faster and accurate performance for searching in problem space with more exploration ability.