Learning behavior in abstract memory schemes for dynamic optimization problems

• Arnold DV, Beyer HG (2006) Optimum tracking with evolution strategies. Evol Comput 14(3):291–308

  Article  Google Scholar 

• Bosman PAN (2007) Learning and anticipation in online dynamic optimization. In: Yang S, Ong YS, Jin Y (eds) Evolutionary computation in dynamic and uncertain environments, chap 6. Springer, Berlin, pp 129–152

• Boumaza AM (2005) Learning environment dynamics from self-adaptation. GECCO workshops 2005. pp 48–54

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

• Branke J (2002) Evolutionary optimization in dynamic environments. Kluwer, Dordrecht

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

• Cover TM, Thomas JA (2006) Elements of information theory. Wiley, Hoboken

  MATH  Google Scholar 

• Fitch R, Hengst B, Suc D, Calbert G, Scholz J (2005) Structural abstraction experiments in reinforcement learning. In: AI 2005 advances in artificial intelligence. pp 164–175

• Ho WC, Nehaniv C, Dautenhahn K (2005) Autobiographic agents in dynamic virtual environments—performance comparison for different memory control architectures. In: Proceedings of the 2005 IEEE congress on evolutionary computing, pp 573–580

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

  Article  Google Scholar 

• Lewis EHJ, Ritchie G (1998) A comparison of dominance mechanisms and simple mutation on non-stationary problems. In: Parallel problem solving from nature-PPSN V, pp 139–148

• Lieberman DA (2004) Learning and memory: an integrative approach. Wadsworth, Belmont

  Google Scholar 

• Michalski RS (2000) Learnable evolution model: evolutionary processes guided by machine learning. Mach Learn 38(1):9–40

  Article  MATH  Google Scholar 

• Mitchell TM (1997) Machine learning. McGraw-Hill, New York

  MATH  Google Scholar 

• Morrison RW, De Jong KA (2000) Triggered hypermutation revisited. In: Proceedings of the 2000 IEEE congress on evolutionary computing, pp 1025–1032

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

  MATH  Google Scholar 

• Richter H (2005) A study of dynamic severity in chaotic fitness landscapes. In: Proceedings of the 2005 IEEE congress on evolutionary computing, pp 2824–2831

• Richter H, Yang S (2008) Memory based on abstraction for dynamic fitness functions. In: EvoWorkshops 2008: applications of evolutionary computing, LNCS, vol 4974, pp 597–606

• Simões A, Costa E (2007) Variable-size memory evolutionary algorithm to deal with dynamic environments. In: EvoWorkshops 2007: applications of evolutionary computing, LNCS, vol 4448, pp 617–626

• Tinós R, Yang S (2007) A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genetic Programming and Evolvable Machines 8(3):255–286

  Article  Google Scholar 

• Yang S (2005) Population-based incremental learning with memory scheme for changing environments. Proc. of the 2005 Genetic and Evol Comput Conf., vol 1, pp 711–718

• Yang S (2006) Associative memory scheme for genetic algorithms in dynamic environments. In: EvoWorkshops 2006: applications of evolutionary computing, LNCS, vol 3907, pp 788–799

• 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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