Global versus local search: the impact of population sizes on evolutionary algorithm performance
作者： Thomas Weise , Yuezhong Wu , Raymond Chiong , Ke Tang , Jörg Lässig
DOI: 10.1007/S10898-016-0417-5
关键词:
摘要: In the field of Evolutionary Computation, a common myth that "An Algorithm (EA) will outperform local search algorithm, given enough runtime and large-enough population" exists. We believe this is not necessarily true challenge statement with several simple considerations. then investigate population size parameter EAs, as element in above claim can be controlled. conduct related work study, which substantiates assumption there should an optimal setting for at specific EA would perform best on problem instance computational budget. Subsequently, we carry out large-scale experimental study 68 instances Traveling Salesman Problem static sizes are powers two between $$(1+2)$$(1+2) $$({262144}+{524288})$$(262144+524288) EAs well adaptive sizes. find analyzing performance different setups over supports our point view existence finite settings.
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David L. Applegate, William J. Cook, Vasek Chvatal, Robert E. Bixby, The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics) Princeton University Press. ,(2007)
Bill P. Buckles, Xiaohui Yuan, Jian Zhang, Multimodal Function Optimization Using Local Ruggedness Information. the florida ai research society. pp. 380- 386 ,(2004)
Wenxiang Chen, Thomas Weise, Zhenyu Yang, Ke Tang, Large-scale global optimization using cooperative coevolution with variable interaction learning parallel problem solving from nature. pp. 300- 309 ,(2010) , 10.1007/978-3-642-15871-1_31
Thomas Jansen, Ingo Wegener, PM Kaufmann, On the utility of populations in evolutionary algorithms genetic and evolutionary computation conference. pp. 1034- 1041 ,(2001)
Xin Yao, Unpacking and understanding evolutionary algorithms world congress on computational intelligence. pp. 60- 76 ,(2012) , 10.1007/978-3-642-30687-7_4
Georges Raif Harik, Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms University of Michigan. ,(1997)
Muhammad Fadhil Mahadzir, Nordin Abu Bakar, The impact of population size on knowledge acquisition in genetic algorithms paradigm: Finding solutions in the game of Sudoku ,(2010)
Ting Hu, Wolfgang Banzhaf, Nonsynonymous to Synonymous Substitution Ratio $k_{\mathrm a}/k_{\mathrm s}$: Measurement for Rate of Evolution in Evolutionary Computation parallel problem solving from nature. pp. 448- 457 ,(2008) , 10.1007/978-3-540-87700-4_45
Peter Bentley, Sanjeev Kumar, Three ways to grow designs: a comparison of embryogenies for an evolutionary design problem genetic and evolutionary computation conference. pp. 35- 43 ,(1999)
Jan Karel Lenstra, David Shmoys, The Traveling Salesman Problem: A Computational Study ,(2007)