A genetic algorithm for solving constrained function optimization problems
AO You-yun · Journal of Yanshan University · 2005
In this paper, a new genetic algorithm is presented to solve constrained function optimization problems. This algorithm can maintain the diversity of the population by using multi-parent crossover and can get high performance by employing minimal generation gap modal to balance the exploration and exploitation to the solution space of the problem effectively. Through the com- puter simulation on two benchmark constrained functions, the numerical experimental results show that this algorithm is effective and stable to solve constrained function optimization problems.