Differential Grouping in Cooperative Co-evolution for Large-Scale Global Optimization: The Experimental Study

Cooperative co-evolution (CC) is a promising method for large-scale optimization problems. The performance of a CC framework is affected greatly by variable grouping. DG2 proposed by Omidvar et al. is accurate for variable grouping. DG2, which is a parameter-free differential grouping method, can distinguish overlapping components of decision variables of a function. In this paper, we test DG2 on high-dimensional functions with more than 1000 dimensions. We also test DG2 on the functions that it is highly imbalanced that the contribution of different components to the overall fitness. The experimental results show that the performance of DG2 is stable as the increase of the dimensionality of the functions, but the grouping accuracy of DG2 drops when the imbalance of contribution of different components to the overall fitness becomes greater and greater.

[1]  Xin Yao,et al.  Large scale evolutionary optimization using cooperative coevolution , 2008, Inf. Sci..

[2]  Xiaodong Li,et al.  Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization , 2014, IEEE Transactions on Evolutionary Computation.

[3]  Xiaodong Li,et al.  Smart use of computational resources based on contribution for cooperative co-evolutionary algorithms , 2011, GECCO '11.

[4]  Masaharu Munetomo,et al.  Linkage Identification by Nonlinearity Check for Real-Coded Genetic Algorithms , 2004, GECCO.

[5]  Kenneth A. De Jong,et al.  A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.

[6]  Xiaodong Li,et al.  Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale , 2009 .

[7]  Xiaodong Li,et al.  DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization , 2017, IEEE Transactions on Evolutionary Computation.

[8]  Xiaodong Li,et al.  A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization , 2016, ACM Trans. Math. Softw..

[9]  Xiaodong Li,et al.  Designing benchmark problems for large-scale continuous optimization , 2015, Inf. Sci..

[10]  Xiaodong Li,et al.  Efficient Resource Allocation in Cooperative Co-Evolution for Large-Scale Global Optimization , 2017, IEEE Transactions on Evolutionary Computation.

[11]  R. Salomon.  Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms. , 1996, Bio Systems.

[12]  Karsten Weicker,et al.  On the improvement of coevolutionary optimizers by learning variable interdependencies , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[13]  Zhenyu Yang,et al.  Large-Scale Global Optimization Using Cooperative Coevolution with Variable Interaction Learning , 2010, PPSN.

[14]  Xingguang Peng,et al.  Large-scale cooperative co-evolution using niching-based multi-modal optimization and adaptive fast clustering , 2017, Swarm Evol. Comput..

[15]  James Demmel,et al.  IEEE Standard for Floating-Point Arithmetic , 2008 .

[16]  Hongfei Teng,et al.  Cooperative Co-evolutionary Differential Evolution for Function Optimization , 2005, ICNC.