A changing range genetic algorithm

During the last decade various methods have been proposed to handle linear and non-linear constraints by using genetic algorithms to solve problems of numerical optimization. The key to success lies in focusing the search space towards 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 to the feasible region; it uses two strategies for estimating a point of attraction. Several test cases demonstrate the ability of this approach to reach effectively and accurately the global optimum with a low resolution of the binary representation scheme and without additional computational efforts