An Improved Genetic Algorithm Based on Van Der Laan-Talman Algorithm

Applying triangulation theory of the Van der laan-Talman algorithm, an improved genetic algorithm is proposed to solve optimal problems in this paper. The algorithm operates on a simplicial subdivision of searching space and generates the integer labels at the vertices, and then crossover operators and increasing dimension operators relying on the integer labels are designed. In this case, whether each individual is a completely labeled simplex can be used as an objective convergence criterion and that determined whether the algorithm will be terminated. Several stander test functions are provided to be examined and the experiment results indicate that the proposed algorithm has higher global optimization capability, computing efficiency and stronger stability.