When dealing with dynamic environments two major aspects must be considered in order to improve the algorithms' adaptability to changes: diversity and memory. In this paper we propose and study a new evolutionary algorithm that combines two populations, one playing the role of memory, with a biological inspired recombination operator to promote and maintain diversity. The size of the memory mechanism may vary along time. The size of the (usual) search population may also change in such a way that the sum of the individuals in the two populations does not exceed an established limit. The two populations have minimum and maximum sizes allowed that change according to the stage of the evolutionary process: if an alteration is detected in the environment, the search population increases its size in order to readapt quickly to the new conditions. When it is time to update memory, its size is increased if necessary. A genetic operator, inspired by the biological process of conjugation, is proposed and combined with this memory scheme. Our ideas were tested under different dynamics and compared with other approaches on two benchmark problems. The obtained results show the efficacy, efficiency and robustness of the investigated algorithm.
Keywords
Evolutionart Computation, Dynamic environments
Subject
Evolutionary Optimization
Conference
EvoWorkshops 2007, April 2007
Cited by
Year 2015 : 1 citations
Zhu, T., Luo, W., & Yue, L. (2015). Dynamic optimization facilitated by the memory tree. Soft Computing, 19(3), 547-566.
Year 2014 : 3 citations
Richter, H. (2014). Fitness Landscapes That Depend on Time. In Recent Advances in the Theory and Application of Fitness Landscapes (pp. 265-299). Springer Berlin Heidelberg.
Zhu, T., Luo, W., & Yue, L. (2014). Dynamic optimization facilitated by the memory tree. Soft Computing, 1-20.
Zhang, Z., Yue, S., Liao, M., & Long, F. (2014). Danger theory based artificial immune system solving dynamic constrained single-objective optimization. Soft Computing, 18(1), 185-206.
Year 2013 : 2 citations
Zhuhong Zhang, Shigang Yue, Min Liao, and Fei Long (2013). Danger theory based artificial immune system solving dynamic constrained single-objective optimization. Soft Computing, pp. 1-22, Springer, 2013.
Hendrik Richter and Shengxiang Yang (2012). Dynamic Optimization Using Analytic and Evolutionary Approaches: A Comparative Review. Zelinka et al. (Eds.): Handbook of Optimization, ISRL 38, pp. 1–28, Springer 2013.
Year 2012 : 2 citations
Hendrik Richter and Shengxiang Yang (2012). Dynamic Optimization Using Analytic and Evolutionary Approaches: A Comparative Review. Zelinka et al. (Eds.): Handbook of Optimization, ISRL 38, pp. 1–28, Springer 2012
Hendrik Richter (2012). Artificial Immune Systems, Dynamic Fitness Landscapes, and the Change Detection Problem. Bio-Inspired Computational Algorithms and Their Applications, pp. 336-350, Dr. Shangce Gao (Ed.), ISBN: 978-953-51-0214-4, InTech, 2012.
Year 2011 : 4 citations
Hendrik Richter, Franz Dietel (2011), “Solving Dynamic Constrained Optimization Problems with Asynchronous Change Pattern”. In C. Di Chio et al. (Eds.): EvoApplications 2011, Part I, LNCS 6624, pp. 334-343, Springer-Verlag Berlin Heidelberg 2011, Torino, Italy, 27-29 April 2011.
H. Meneses Ponce, M. Inostroza-Ponta (2011). Evaluating memory schemas in a Memetic Algorithm for the Quadratic Assignment Problem. XXX International Conference of the Chilean Computer Science Society (SCCC), Chile 2011.
H. Meneses Ponce, M. Inostroza-Ponta (2011). Esquemas de Memoria en Metaheuristicas: Mejora de un Algoritmo Memetico para el Problema de Asignacion Cuadratica. Jornadas Chilenas de Computacion, Chile 2011.
Tao Zhu, Wenjian Luo, Zhifang Li (2011). An adaptive strategy for updating the memory in Evolutionary Algorithms for dynamic optimization. 2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp.8-15, IEEE 2011.
Year 2010 : 5 citations
H. Richter (2010). "Evolutionary Optimization and Dynamic Fitness Landscapes. Evolutionary Algorithms and Chaotic Systems, Studies in Computational Intelligence, Vol. 267/2010, pp. 409-446, Springer, 2010.
H. Richter (2010). Memory Design for Constrained Dynamic Optimization Problems. C. Di Chio et al. (Eds.): EvoApplications 2010, Part I, LNCS 6024, pp. 552"561, Springer-Verlag Berlin Heidelberg, 2010.
Hao Chen, Ming Li, Xi Chen (2010), A Predator-Prey Cellular Genetic Algorithm for Dynamic Optimization Problems. 2nd International Conference on Information Engineering and Computer Science (ICIECS), pp. 1-6, IEEE 2010.
Hao Chen, Ming Li, Xi Chen (2010), Hybrid Memory Scheme for Genetic Algorithm in Dynamic Environments. Journal of Applied Sciences - Electronics and Information Engineering, Vol 28, nº 5, pp. 540-545, 2010.
Hao Chen, Ming Li, Xi Chen (2010), Cellular Genetic Algorithm with Density Dependence for Dynamic Optimization Problem. Journal of Information and Computing Science, Vol. 5, No. 4, pp. 287-298, World Academic Press 2010.
Year 2009 : 4 citations
J. Tim Hendtlass, Irene Moser, Marcus Randal (2009). Dynamic Problems and Nature Inspired Meta-heuristics. Biologically-Inspired Optimisation Methods , Series Studies in Computational Intelligence, Volume 210, pp. 79-109, Springer 2009.
H. Richter (2009). Detecting change in dynamic fitness landscapes, pp.1613-1620, 2009 IEEE Congress on Evolutionary Computation, IEEE Press, 2009.
H. Richter, S. Yang (2009). Learning behavior in abstract memory schemes for dynamic optimization problems. Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 13, Number 12, pp. 1163-1173, Springer 2009.
H. Richter (2009). Change detection in dynamic fitness landscapes: An immunological approach. In: World Congress on Nature and Biologically Inspired Computing (NaBIC'09), (Eds.: A. Abraham, A. Carvalho, F. Herrera, V. Pai), IEEE Research Publishing Services, Singapore, 719-724, IEEE Press, 2009.
