A genetic algorithm for solving the CEC'2013 competition problems on real-parameter optimization
Saber Mohammed Elsayed, Ruhul Amin Sarker, Daryl Essam · 2013
Many genetic algorithms variants have been introduced for solving different classes of optimization problems. The success of any GA depends on the design of its search operators, as well as its parameters. In this paper, we propose a new three-parent crossover. In addition, we design a diversity operator which works with an archive of selected individuals. The algorithm has been applied to solve all the CEC'2013 competition problems on real-parameter optimization. The solutions obtained are either optimal or very close to the known best solutions.