New evolutionary modifications in non-stationary environments

• Autorzy: Wawrzyniak, D. , Obuchowicz, A.
• Konferencja: Evolutionary Computation and Global Optimization (10; Krajowa Konferencja Algorytmy Ewolucyjne i Optymalizacja Globalna; 11-13.06.2007; Będlewo, Poland)
• Języki publikacji: EN

Abstrakty

EN

The paper deals with the evolutionary algorithm which uses new methods which allow to increase the efficiency of finding the optimum in an environment in which fitness function is time-varying. All methods base on gathering information from environments obtained with the help of indyviduals (treated as sensors) and use it to help to make a decision to use a particular mechanism. These methods include watching procedure which provides information about changes in the environment based on using individuals as detectors, multiply random immigrants mechanism which investigates a particular area of the environment, predict procedure which tries to calculate the next location of the optimum, memory mechanism which cannot be classified as any existing type of memory.

Słowa kluczowe

EN

evolutionary algorithm; environment changes

• Czasopismo: Prace Naukowe Politechniki Warszawskiej. Elektronika
• Rocznik: 2007
• Tom: z. 160
• Strony: 285-292
• Opis fizyczny: Bibliogr. 10 poz., tab., schem.

Twórcy

autor

Wawrzyniak, D.

autor

Obuchowicz, A.

• Faculty of Electrical Engineering, Computer Science and Telecommunications Zielona Góra,

Bibliografia

• [1] A. Obuchowicz, D. Wawrzyniak, Evolutionary Adaptation in Non-stationary Environments: a Case Study. In: R. Wyrzykowski, J. Dongarra, N. Meyer, J. Waśniewski (Eds.) Parallel Processing And Applied Mathematics, Springer (LNSC 3911) 2005, pp. 439-446.
• [2] C. N. Bendtsen. Optimization of Non-Stationary Problems with Evolutionary Algorithms and Dynamic Memory. Aarhus Universitet, 2001.
• [3] J. Branke. Memory-enhanced evolutionary algorithms for dynamic optimization problems. Proc. IEEE Congress on Evolutionary Computation, CEC 1999, volume 3 pp. 1875-1882.
• [4] T. Drink, C. N. Bendtsen. Dynamic Memory Model for Non-Stationary Optimization.
• [5] D. Wawrzyniak, A. Obuchowicz. New Approach To Fast And Precise Optimization With Evolutionary Algorithm. In: J. Arabas (Ed.) Evolutionary Computation and Global Optimization 2006, Warsaw University of Technology Press (series: Electronics 156) 2006, pp. 397-404.
• [6] D. Wawrzyniak, A. Obuchowicz. New Approach To Optimization With Evolutionary Algorithm in Dynamic Environment. Proceedings of Artificial Intelligence Studies. University of Podlasie Press 2006, Vol. 3, pp. 187-196 .
• [7] D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. - Addison-Wesley, Reading, MA., 1989.
• [8] A. Obuchowicz. Evolutionary Algorithms for Global Optimization and Dynamic System Diagnosis. Lubuskie Scientific Society Press, 2003.
• [9] K. Trojanowski. Evolutionary Algorithm with Redundant Genetic Material for Nonstatinary Environments. Polish Academy of Science, 2003.
• [10] A. C. Rosa V. Ramos, C. Fernandes. Societal Implicit Memory and his Speed on Tracking Extrema in Dynamic Environments using Self-Regulatory Swarms. Technical University of Lisbon, 2006.