Environment identification-based memory scheme for estimation of distribution algorithms in dynamic environments

• Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 IEEE congress on evolutionary computation (CEC99), vol 3, pp 1875–1882

• Branke J (2001) Evolutionary optimization in dynamic environments, Kluwer, Dordrecht

• Branke J, Kaubler T, Schmidt C, Schmeck H (2000) A multipopulation approach to dynamic optimization problems. In: Proceedings of the 4th international conference on adaptive computing in design and manufacturing, pp 299–308

• Branke J, Lode C, Shapiro JL (2007) Addressing sampling errors and diversity loss in UMDA. In: Proceedings of the 9th annual conference on genetic and evolutionary computation (GECCO 2007), pp 508–515

• Cobb HG (1990) An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Techincal Report AIC-90-001, Naval Research Lab, Washington, USA

• Cedeno W, Vemuri VR (1997) On the use of niching for dynamic landscapes. In: Proceedings of the 1997 IEEE international conference on evolutionary computation, pp 361–366

• Goldberg DE (2002) The design of innovation: lessons from and for competent genetic algorithms. Kluwer, Norwell

  MATH  Google Scholar 

• Grefenstette JJ, Fitzpatrick J (1992) Genetic algorithms for changing environments. In: Proceedings of the 2nd internatioanl conference on parallel problem solving from nature

• Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments—a survey. IEEE Trans Evol Comput 9(3):303–317

  Article  Google Scholar 

• Mori N, Kita H, Nishikawa Y (1996). Adaptation to a changing environment by means of the thermodynamical genetic algorithm. In: Ebeling W et al. (eds) Proceedings of the 4th international conference on parallel problem solving from nature (PPSN IV), pp 513–522

• Morrison R (2004) Designing evolutionary algorithms for dynamic environments, Springer, Berlin

• Morrison R, De Jong K (1999) A test problem generator for non-stationary environments. In: Proceedings of the 1999 congress on evolutionary computation, pp 2047–2053

• Mühlenbein H, Paaß G (1996) Recombination of genes to the estimation of distributions. In: Ebeling W (ed) Proceedings of the 4th international conference on parallel problem solving from nature (PPSN IV), pp 178–187

• Pelikan M (2002) Bayesian optimization algorithm: from single level to hierarchy. PhD thesis. University of Illinois, Urbana-Champaign, USA

• Shapiro JL (2003) Scaling of probability-based optimization algorithms. In: Obermayer K (ed) Advances in neural information processing systems, pp 399–406

• Shapiro JL (2005) Drift and scaling in estimation of distribution algorithms. Evol Comput 13(1):99–123

  Article  Google Scholar 

• Shapiro JL (2006) Diversity loss in general estimation of distribution algorithms. In: Runarsson TP (ed) Proceedings of the 9th international conference on parallel problem solving from nature, LNCS 4193, pp 92–101

• Ursem RK (2000) Multinational GA optimization techniques in dynamic environments. In: Proceedings of the 2000 genetic and evolutionary computation conference (GECCO 2000), pp 19–26

• Whitley LD (1991) Fundamental principles of deception in genetic search. In: Rawlins GJE (ed) Foundations of genetic algorithms. Morgan Kaufmann, San Francisco, pp 221–241

• Wineberg M, Oppacher F (2000) Enhancing the GA’s ability to cope with dynamic environments. In: Proceedings of the 2000 Genetic and Evolitionary Computation Conference (GECCO 2000), pp 3–10

• Yang S (2003) Non-stationary problem optimization using the primal-dual genetic algorithm. In: Proceedings of the 2003 IEEE congress on evolutionary computation, vol 3, pp 2246–2253

• Yang S (2005a) Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol 3, pp 2560–2567

• Yang S (2005b) Population-based incremental learning with memory scheme for changing environments. In: Proceedings of the 7th annual conference on genetic and evolutionary computation (GECCO 2005), pp 711–718

• Yang S (2006) Associative memory scheme for genetic algorithms in dynamic environments. In: EvoWorkshops 2006: applications of evolutionary computing, pp 788–799

• Yang S, Yao X (2005) Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput 9(11):815–834

  Article  MATH  Google Scholar 

• Yang S, Yao X (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Trans Evol Comput 12(5):542–561

  Article  Google Scholar 

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