Yun-Geun Lee
ABSTRACT
All evolutionary algorithms trade off exploration and exploitation in optimisation problems; dynamic problems are no exception. We investigate this trade-off, over a range of algorithm settings, on dynamic variants of three well-known optimisation problems (One Max, Royal Road and knapsack), using Yang's XOR method to vary the scale and rate of change. Extremely exploitative algorithm settings performed best for a surprisingly wide range of problems; even where they were not the most effective, they still performed competitively, and even in those cases, the best performers were still far more exploitative than most would anticipate.
- G. Dantzig. Discrete-variable extremum problems. Operations Research, pages 266--277, 1957.Google Scholar
- J. Grefenstette. Genetic algorithms for changing environments. Parallel problem solving from nature, 2:137--144, 1992.Google Scholar
- Y. Jin and J. Branke. Evolutionary optimization in uncertain environments -- a survey. IEEE Transactions on Evolutionary Computation, 9(3):303--317, June 2005. Google ScholarDigital Library
- L. Kang, A. Zhou, B. McKay, Y. Li, and Z. Kang. Benchmarking algorithms for dynamic travelling salesman problems. In Evolutionary Computation, 2004. CEC2004. Congress on, volume 2, pages 1286--1292 Vol.2, June 2004.Google Scholar
- C. Li, S. Yang, T. T. Nguyen, E. L. Yu, X. Yao, Y. Jin, H. g. Beyer, and P. N. Suganthan. Benchmark generator for cec'2009 competition on dynamic optimization. Technical report, Dept of Computer Science, University of Leicester, 2008.Google Scholar
- C. MacNish. Towards unbiased benchmarking of evolutionary and hybrid algorithms for real-valued optimisation. Connection Science, 19(4):361--385, 2007. Google ScholarDigital Library
- M. Mitchell, S. Forrest, and J. Holland. The royal road for genetic algorithms: Fitness landscapes and GA performance. In Towards a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, pages 245--254, 1992.Google Scholar
- P. Rohlfshagen and X. Yao. Dynamic combinatorial optimisation problems: an analysis of the subset sum problem. Soft Computing-A Fusion of Foundations, Methodologies and Applications, pages 1--12, to appear. Google ScholarDigital Library
- J. Schaffer and L. Eshelman. On Crossover as an Evolutionary Viable Strategy. In R. Belew and L. Booker, editors, Proceedings of the 4th International Conference on Genetic Algorithms, pages 61--68. Morgan Kaufmann, 1991.Google Scholar
- K. Tang, X. Yao, P. Suganthan, C. MacNish, Y. Chen, C. Chen, and Z. Yang. Benchmark functions for the cec'2008 special session and competition on large scale global optimization. Technical report, Nature Inspired Computation and Applications Laboratory, USTC China, 2008.Google Scholar
- D. Wolpert, W. Macready, I. Center, and C. San Jose. No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1):67--82, 1997. Google ScholarDigital Library
- S. Yang. Non-stationary problem optimization using the primal-dual genetic algorithm. In Proceedings of the 2003 IEEE Congress on Evolutionary Computation, volume 3, pages 2246--2253. IEEE Press, 2003.Google Scholar
- S. Yang. Genetic algorithms with elitism-based immigrants for changing optimization problems. In Proceedings of the European Evolutionary Computing Workshops (Evoworkshops), volume 4448 of Springer Lecture Notes in Computer Science, pages 627--636, Berlin, 2007. Springer-Verlag. Google ScholarDigital Library
- S. Yang and R. Tinos. Hyper-selection in dynamic environments. In Proceedings of the 2008 IEEE Congress on Evolutionary Computation, pages 3185--3192. IEEE Press, 2008.Google Scholar
- S. Yang and X. Yao. Population-based incremental learning with associative memory for dynamic environments. IEEE Transactions on Evolutionary Computation, 12(5):542--561, Oct 2008. Google ScholarDigital Library
Index Terms
- How hard should we run?
Recommendations
Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change
Evolutionary dynamic optimisation has become one of the most active research areas in evolutionary computation. We consider the Balance function for which the poor expected performance of the ( 1 + 1 ) EA at low frequencies of change has been shown in ...
Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change
GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computationEvolutionary dynamic optimisation has become one of the most active research areas in evolutionary computation. We consider the BALANCE function for which the poor performance of the (1+1) EA at low frequencies of change has been shown in the ...
Differential evolution with spatially neighbourhood best search in dynamic environment
In recent years, there has been a growing interest in applying differential evolution (DE) to optimisation problems in a dynamic environment. The ability of tracking a changing optimum over time is concerned in dynamic optimisation problems (DOPs). In ...
Comments