Ensemble strategies with adaptive evolutionary programming
Ensemble strategies with adaptive evolutionary programming
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DOI：
10.1016/j.ins.2010.01.007
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发表时间：
2010-05
期刊：
影响因子：
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通讯作者：
R. Mallipeddi;S. Mallipeddi;P. Suganthan
中科院分区：
文献类型：
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作者：
R. Mallipeddi;S. Mallipeddi;P. Suganthan
变异算子，如高斯，列维和柯西已被用于进化编程（EP）。根据没有免费的午餐定理，它是不可能的EP与一个单一的变异算子总是优于。例如，具有高斯突变的经典EP（CEP）在局部邻域中搜索得更好，而具有柯西突变的快速EP（FEP）在更大的邻域中执行得更好。受这些观察的启发，我们提出了一种集成方法，其中每个突变算子都有其相关的种群，每个种群都从每个函数调用中受益。这种方法使我们能够受益于不同的变异算子与不同的参数值，只要他们是有效的搜索过程的不同阶段。此外，最近提出的自适应EP（AEP）使用高斯（ACEP）和柯西（AFEP）突变也进行了评估。在AEP中，策略参数值是根据前几代算法的搜索性能进行调整的。集成的性能进行了比较与混合变异策略，它集成了几个变异算子到一个单一的算法，以及对AEP与一个单一的变异算子。通过统计测试，验证了该集成算法相对于单一变异算法和混合变异算法的性能改善。
Mutation operators such as Gaussian, Lévy and Cauchy have been used with evolutionary programming (EP). According to the no free lunch theorem, it is impossible for EP with a single mutation operator to outperform always. For example, Classical EP (CEP) with Gaussian mutation is better at searching in a local neighborhood while the Fast EP (FEP) with the Cauchy mutation performs better over a larger neighborhood. Motivated by these observations, we propose an ensemble approach where each mutation operator has its associated population and every population benefits from every function call. This approach enables us to benefit from different mutation operators with different parameter values whenever they are effective during different stages of the search process. In addition, the recently proposed Adaptive EP (AEP) using Gaussian (ACEP) and Cauchy (AFEP) mutations is also evaluated. In the AEP, the strategy parameter values are adapted based on the search performance in the previous few generations. The performance of ensemble is compared with a mixed mutation strategy, which integrates several mutation operators into a single algorithm as well as against the AEP with a single mutation operator. Improved performance of the ensemble over the single mutation-based algorithms and mixed mutation algorithm is verified using statistical tests.