A combined adaptive bounding and adaptive mutation technique for genetic algorithms

A combined adaptive bounding and adaptive mutation technique is proposed both to improve the solution precision and to increase the convergence rate of genetic algorithms for continuous optimization problems. The proposed technique is tested over two benchmark continuous functions, and the results show that the proposed technique is superior to simple GAs and GAPSSA.