Yong Liang
ABSTRACT
This paper introduces a novel genetic variable representation for dynamic optimization problems in evolutionary computation. This variable representation allows static evolutionary optimization approaches to be extended to efficiently explore global and better local optimal areas in dynamic fitness landscapes. It represents a single individual as a pair of real-valued vector (x, r) ∈ Rn x R2 in the evolutionary search population. The first vector x corresponds to a point in the n-dimensional search space (an object variable vector), while the second vector r represents the dynamic fitness value and the dynamic tendency of the individual x in the dynamic environment. r is the control variable (also called strategy variable), which allow self-adaptation. The object variable vector x is operated by different genetic strategies according to its corresponding r. As a case study, we have integrated the new variable representation into Genetic Algorithms (GAs), yielding an Dynamic Optimization Genetic Algorithm (DOGA). DOGA is experimentally tested with 5 benchmark dynamic problems. The results all demonstrate that DOGA consistently outperforms other GAs on dynamic optimization problems.
- Branke J., Schmidt C., and Schmeck H., Efficient fitness estimation in noisy environments. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 243--250, 2001Google Scholar
- Darwen P. J., Computationally intensive and noisy tasks: Coevolutionary learning and temporal difference learing on Backgammon, Proceeding of the 2000 Congress on Evolutionary Computation, CEC-2000, pp. 872--879, IEEE Press, 2000Google Scholar
- Fogel L. J., Owens A. J. and Walsh M. J., Artificial Intelligence through Simulated Evolution, John Wiley \& Sons, New York, 1996Google Scholar
- Kennedy J. and Eberhart R. C., Particle swarm optimization, Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, pp. 1942--1948, IEEE Press, 1995Google ScholarCross Ref
- Markon S., Arnold D. V., Baeck T., Beielstein T. and Beyer H. G., Thresholding - a selection operator for noisy es, Proceedings of the 2001 Congress on Evolutionary Computation, pp. 465--472, 2001Google ScholarCross Ref
- Matsumura Y., Ohkura K. and Ueda K., Evolutionary dynamics of evolutionary programming in noisy environments, Proceedings of the 2001 Congress on Evolutionary Computation, pp. 17--24, IEEE Press, 2001Google ScholarCross Ref
- Leung K. S. and Liang Y., Adaptive elitist-population based genetic algorithm for multimodal function optimization, Proceedings of the 2003 Genetic and Evolutionary Computation Conference (GECCO-2003), pp. 1160--1171, 2003 Google ScholarDigital Library
- Li J. P., Balazs M. E., Parks G. T. and Glarkson P. J., A species conserving genetic algorithms for multimodal function optimization, Evolutionary Computation, vol. 10, no. 3, pp. 207--234, 2002 Google ScholarDigital Library
- Liang Y., Leung K. S. and Mok S. K., A novel evolutionary drug scheduling model in cancer chemotherapy, IEEE Trans. Information Technology in Biomedicine, vol. 10, pp. 237--245, 2006 Google ScholarDigital Library
- Paterlini S. and Krink K., High performance clustering using differential evolution, Proceedings of the Six Congress on Evolutionary Computation (CEC-2004), pp. 68--74, IEEE Press, 2004Google Scholar
- Petrowski A., A Clearing procedure as a niching method for genetic algorithms, Proceeding of the 1996 Congress on Evolutionary Computation (CEC-1996), pp. 798--803, IEEE Press, 1996Google ScholarCross Ref
- Price K. V., An introduction to differential evolution, New Ideas in Optimization}, pp. 79--108, 1999 Google ScholarDigital Library
- Rechenberg I., Evolution Strategy: Optimization of Technical Systems by Means of Biological Evolution, Fromman-Holzboog, Stuttgart, 1973Google Scholar
- Rudolph G., A partial order approach to noisy fitness functions, Proceedings of the 2001 Congress on Evolutionary Computation, (CEC-2001), pp. 318--325, 2001Google ScholarCross Ref
- Sano Y. and Kita H., Optimization of noisy fitness functions by means of genetic algorithms using history of search with test of estimation, Proceedings of the 2002 Congress on Evolutionary Computation, CEC-2002, pp. 360--365, IEEE Press, 2002Google ScholarCross Ref
- Shi Y. and Eberhart R. C., Parameter selection in particle swarm optimization, Lecture Notes in Computer Science}, vol. 1447, pp. 591--600, 1988 Google ScholarDigital Library
- Tamaki H and Arai T., A genetic algorithm approach to optimization problems in an uncertain environment, {\it Proceedings of the 1997 International Conference on Neural Information}, vol. 1, pp. 436--439, 1997Google Scholar
- Thomsen R., Flexible ligand docking using differential evolution, Proceedings of the 2003 Congress on Evolutionary Computation, CEC-2003, pp. 2354--2361, IEEE Press, 2003Google ScholarCross Ref
- Thiemo K., Bogdan F. and Fogel G. B., Noisy optimization problems --- a particular challenge for differential evolution? Proceedings of the 2004 Congress on Evolutionary Computation, CEC-2004, pp. 332--339, IEEE Press, 2004Google Scholar
- Ursem R. K. and Vadstrup P., Parameter identification of induction motors using differential evolution, Proceedings of the 2003 Congress on Evolutionary Computation, CEC-2003, pp. 790--796, IEEE Press, 2003Google ScholarCross Ref
- Vesterstrom J. S. Riget J. and Krink T., Division of labor in particle swarm optimization, Proceedings of the 2002 Congress on Evolutionary Computation}, CEC-2002, pp. 1570--1575, IEEE Press, 2002 Google ScholarDigital Library
- Wolpert D. H. and Macready W. G., No free lunch theorems for optimization, IEEE Trans. On Evolutionary Computation, vol. 1, pp. 67--82, 1997 Google ScholarDigital Library
Index Terms
- A novel genetic variable representation for dynamic optimization problems in evolutionary computation
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