An efficient real-coded genetic algorithm for real-parameter optimization

In this paper, we present an efficient real-coded genetic algorithm. In the proposed genetic algorithm model, crossover and mutation behaviors are performed by similarity between individuals. The proposed real coded genetic algorithm is compared with three existing genetic algorithms. A set of 18 test problems available in the global optimization literature is used to evaluate the performance of proposed genetic algorithm. The comparative study shows that the proposed genetic algorithm performs quite well and outperforms other algorithms.