The development of a changing range genetic algorithm

Genetic algorithms are nature-inspired heuristics for search and optimization. The key to success lies in focusing the search space on a feasible region where a global optimum is located. This study investigates an approach that adaptively shifts and shrinks the size of the search space of the feasible region by employing feasible and infeasible solutions in the population to reach the global optimum. Several test cases demonstrate the ability of this approach to improve significantly the speed of convergence to the global optimum with reasonable precision.