A new genetic algorithm for global optimization of multimodal continuous functions

• A new real coded parent centric crossover operator, double Pareto crossover (DPX) is defined.
• A new genetic algorithm (DPX–PM) for unconstrained continuous global optimization is designed using DPX and power mutation (PM).
• DPX–PM outperforms six existing algorithms on a set of 27 global optimization problems.

In this paper a new genetic algorithm is developed to find the near global optimal solution of multimodal nonlinear optimization problems. The algorithm defined makes use of a real encoded crossover and mutation operator. The performance of GA is tested on a set of twenty-seven nonlinear global optimization test problems of variable difficulty level. Results are compared with some well established popular GAs existing in the literature. It is observed that the algorithm defined performs significantly better than the existing ones.