Empirical Evaluation of Changing Crossover Operators to Solve Function Optimization Problems
Empirical Evaluation of Changing Crossover Operators to Solve Function Optimization Problems
复制标题
改变交叉算子解决函数优化问题的实证评估
DOI：
10.1109/ssci.2016.7850141
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Ryouei Takahashi
中科院分区：
文献类型：
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作者：
Naohiro Ishii;Ippei Torii;Kazunori Iwata;Kazuya Odagiri;Toyoshiro Nakashima;Kanata Isobe and Hiroyuki Torikai;Ryouei Takahashi
为了解决遗传算法中的早熟收敛问题，本文对改变交叉算子（CXO）求解函数优化问题（FOP）的有效性进行了实验验证。CXO是通过遗传算法（GA）寻找组合优化问题的解的方法，同时保持满足GA的相反条件的平衡：维持种群的多样性并提高搜索解的效率。CXO测量一代中每一端种群的多样性，并根据多样性程度动态地交替全局搜索方法和局部搜索方法。通过上述设计，与只使用一种交叉算子进行交叉操作相比，CXO可以降低陷入局部最优的概率。在FOP中，考虑到函数的连续性，提出了BLX-α（Blend Crossover）和SPX（Simplex Crossover）等实数编码的交叉算子，并将其添加到两点交叉算子等一般交叉算子中。在我们的调查中，我们研究了当生成的孩子的多样性低于所需阈值时，将两点交叉交换为BLX-α和将SPX交换为BLX-α的CXO。通过实验验证了CXO比单独使用的两点交叉、BLX-α和SPX等交叉算子更能提高搜索解的精度。两个著名的多峰函数，舒伯特的测试功能和六峰骆驼背功能也在本报告中进行了研究。
In this paper, the effectiveness of methodologies for changing crossover operators (CXOs) to solve function optimization problems (FOP) are empirically validated in order to solve the problems of premature convergence in genetic algorithms. CXOs are methods of finding solutions for combinatorial optimization problems through genetic algorithms (GAs) while maintaining the balance of satisfying the contrary requisites for GAs: to sustain the diversity of the population and to improve the efficiency of searching for solutions. CXOs measure the diversity of the population on each end of a generation and dynamically alternate global search methods and local search methods according to the degree of that diversity. With the above devices, CXOs can decrease the probability of falling into local optima compared to using only one kind of crossover operator in the crossover operation. In FOP, considering the continuity of functions, real-coded crossover operators such as BLX-α (Blend Crossover) and SPX (Simplex Crossover) are invented and added to general crossover operators such as Two-point Crossover operators. In our investigation, we studied CXOs that exchange Two-point crossover for BLX-α and SPX for BLX-α when the diversity of the generated children drops below the required threshold. We verified experimentally that CXOs can improve the accuracy of searched solutions beyond singly used crossover operators such as Two-point Crossover, BLX-α and SPX. Two well-known multimodal functions are also investigated in this report-Shubert's test function and the Six-hump camel back function.