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首页> 外文会议>Computational Intelligence and Security (CIS), 2011 Seventh International Conference on >A Genetic Algorithm Based on a New Fitness Function for Constrained Optimization Problem
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A Genetic Algorithm Based on a New Fitness Function for Constrained Optimization Problem

机译：基于新适应度函数的遗传算法求解约束优化问题

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摘要

According to the characteristics of constrained optimization problem, a new approach based on a new fitness function is presented to handle constrained optimization problems. The primary features of the algorithm proposed are as follows. Inspired by the smooth function technique, a new fitness function is designed which can automatically search potential solutions. In order to make the fitness function work well, a special technique which keeps a certain number of feasible solutions is also used. In addition, new genetic operators are proposed to enhance the proposed algorithm, i.e., crossover operator and mutation operator are designed according to whether the parent solution is a feasible solution or not. Also, to accelerate the algorithm convergence speed, one dimensional search scheme is incorporated into the crossover operator. At last, the computer simulation demonstrates the effectiveness of the proposed algorithm.
机译：根据约束优化问题的特点，提出了一种基于新适应度函数的新方法来处理约束优化问题。提出的算法的主要特征如下。受平滑函数技术启发，设计了一种新的适应性函数，该函数可以自动搜索潜在的解决方案。为了使适应度函数正常工作，还使用了一种保留一定数量可行解的特殊技术。另外，提出了新的遗传算子以增强所提出的算法，即，根据父代解是否可行来设计交叉算子和变异算子。另外，为了加快算法的收敛速度，将一维搜索方案结合到交叉算子中。最后，计算机仿真证明了该算法的有效性。

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