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An evolutionary algorithm to track changes of optimum value locations in dynamic environments
Keywords:
Evolutionary Algorithm, Dynamic Environments, Genetic Diversity, Macromutation OperatorsAbstract
Non-stationary, or dynamic, problems change over time. There exist a variety of forms of dynamism. The concept of dynamic environments in the context of this paper means that the fitness landscape changes during the run of an evolutionary algorithm. Genetic diversity is crucial to provide the necessary adaptability of the algorithm to changes. Two mechanism of macromutation are incorporated to the algorithm to maintain genetic diversity in the population. The algorithm was tested on a set of dynamic testing functions provided by a dynamic fitness problem generator. The main goal was to determinate the algorithm´s ability to reacting to changes of optimum values that alter their locations, so that the optimum value can still be tracked when dimensional and multimodal scalability in the functions is adjusted. The effectiveness and limitations of the proposed algorithm is discussed from results empirically obtained.
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References
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[16] R.W. Morrison and K. De Jong – “A Test Problem Generator for Non-Stationary Environments”, Proceedings of Congress on Evolutionary Computation, VIII, pp 2047 – 2053, Washington DC, USA, IEEE Service Center, 1999.
[17] K. Trojanowsky – “Evolutionary Algorithms with Redundant Genetic Material for NonStationary Environments”, PhD. Thesis, Institute of Computer Science, Warsaw, University of Technology, Poland, 1994.
[2] J. Branke, - “ Evolutionary Optimization in Dynamic Environments”, Kluwer Academic Publishers, 2002.
[3] D. E. Golberg, and R. E. Smith,– “Nonstationary Function Optimization using Genetic Algorithms with Dominance and Diploid”, Proceedings of the Second International Conference on Genetic Algorithms, pp. 59-68, Lawrence Erlbaum Associates, 1987.
[4] H.G. Cobb – “An Investigation into the use of Hypermutation as an Adaptive Operator in Genetic Algorithms having Continuous, Time_Dependent Non-Stationary Environments”, Technical Report 6760, Naval Research Laboratory, USA, 1990.
[5] J. J. Grenfenstette,– “Genetic Algorithms for Changing Environments”, Proceedings of the Second Conference on Parallel Problem Solving from Nature”, pp. 137 –144, North Holland, 1992.
[6] N. S. Mori, et al. – “Adaptation to Changing Environments by means of the Memory Based Thermodynamic Genetic Algorithm”, Proceeding of the Seventh International Conference on Genetic Algorithms, pp 299 – 306, Morgan Kaufmann, 1997.
[7] J. Lewis, E. Hart and G. Ritchie – “ A Comparison of Dominance Mechanism and Simple Mutation in Non-Stationary Problems”, Proceeding of the Seventh International Conference on Genetic Algorithms, pp 138-148, Morgan Kaufmann, 1997.
[8] T. Bäck – “On the Behaviour of Evolutionary Algorithms in Dynamic Fitness Landscapes”, Proceeding of IEEE International Conference on Evolutionary Algorithms, pp 446-451, IEEE Service Centre, 1998.
[9] C. O. Wile - “Evolutionary Dynamics in TimeDependent Environments”, PhD. Thesis, Institut für Neuroinformatik, Ruhr-Universität, Bochum, Germany, 1999.
[10] K. Trojanoswky and Z. Michalewicz – “Searching for Optima in Non-Stationary Environments”, Proceeding of Congress on Evolutionary Computation, pp 1843-1850, IEEE Service Centre, 1999.
[11] K. Wicker and N. Weicker – “ On Evolutionary Strategy Optimization in Dynamic Environments”, Proceeding of Congress on Evolutionary Computation, pp 2039-2046, IEEE Service Centre, 1999.
[12] W. Liles and K. De Jong. – “The Usefulness of Tag Bits in Changing Environments”, Proceeding of Congress on Evolutionary Computation, pp 2054-2060, IEEE Service Centre, 1999.
[13] W.Yi, Q. Lin, and Y. He – “Dynamic Distributed Genetics Algorithms”, Proceeding of Congress on Evolutionary Computation, pp 1132-1135, IEEE Service Centre, 2000.
[14] T. Nanayakkara, et al. – “ Adaptive Optimization in a Class of Dynamic Environments using an Evolutionary Approach”, Journal of Evolutionary Computation, 7(1):45-68, 1999.
[15] R. W. Morrison – “ Designing Evolutionary Algorithms for Dynamic Environments”, PhD Thesis, George Mason University, USA, 2002
[16] R.W. Morrison and K. De Jong – “A Test Problem Generator for Non-Stationary Environments”, Proceedings of Congress on Evolutionary Computation, VIII, pp 2047 – 2053, Washington DC, USA, IEEE Service Center, 1999.
[17] K. Trojanowsky – “Evolutionary Algorithms with Redundant Genetic Material for NonStationary Environments”, PhD. Thesis, Institute of Computer Science, Warsaw, University of Technology, Poland, 1994.
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Published
2004-10-01
How to Cite
Aragón, V. S., & Esquivel, S. C. (2004). An evolutionary algorithm to track changes of optimum value locations in dynamic environments. Journal of Computer Science and Technology, 4(03), p. 127–133. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/890
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