Learning behavior in abstract memory schemes for dynamic optimization problems

Soft Computing

Volumn 13, Issue 12, 2009, Pages 1163-1173

  Richter, Hendrik ^a   Yang, Shengxiang ^b  

^a LEIPZIG UNIVERSITY OF APPLIED SCIENCES   (Germany)

^b UNIVERSITY OF LEICESTER   (United Kingdom)

Author keywords

Dynamic optimization problem; Evolutionary algorithm; Learning; Memory dynamics

Indexed keywords

DYNAMIC ENVIRONMENTS; DYNAMIC OPTIMIZATION PROBLEM; DYNAMIC OPTIMIZATION PROBLEMS; EXPERIMENTAL STUDIES; LEARNING; LEARNING AND MEMORY; LEARNING BEHAVIOR; LEARNING PROCESS; MEMORY DYNAMICS; MEMORY SCHEMES;

ABSTRACTING; EDUCATION; LEARNING ALGORITHMS; OPTIMIZATION; PROBLEM SOLVING;

EVOLUTIONARY ALGORITHMS;

EID: 67749103518     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-009-0420-6     Document Type: Conference Paper

References (23)

1. Arnold DV, Beyer HG (2006) Optimum tracking with evolution strategies. Evol Comput 14(3):291-308
2. 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
3. Boumaza AM (2005) Learning environment dynamics from self-adaptation. GECCO workshops 2005. pp 48-54
4. Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 IEEE congress on evolutionary computation. pp 1875-1882
5. Branke J (2002) Evolutionary optimization in dynamic environments. Kluwer, Dordrecht
6. 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
7. Cover TM, Thomas JA (2006) Elements of information theory. Wiley, Hoboken
8. 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
9. 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
10. Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments - a survey. IEEE Trans Evol Comput 9(3):303-317
11. 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
12. Lieberman DA (2004) Learning and memory: An integrative approach. Wadsworth, Belmont
13. Michalski RS (2000) Learnable evolution model: Evolutionary processes guided by machine learning. Mach Learn 38(1):9-40
14. Mitchell TM (1997) Machine learning. McGraw-Hill, New York
15. Morrison RW, De Jong KA (2000) Triggered hypermutation revisited. In: Proceedings of the 2000 IEEE congress on evolutionary computing, pp 1025-1032
16. Morrison RW (2004) Designing evolutionary algorithms for dynamic environments. Springer, Berlin
17. 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
18. Richter H, Yang S (2008) Memory based on abstraction for dynamic fitness functions. InA EvoWorkshops 2008: applications of evolutionary computing, LNCS, vol 4974, pp 597-606
19. 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
20. 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
21. 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
22. Yang S (2006) Associative memory scheme for genetic algorithms in dynamic envirAnments. In: EvoWorkshops 2006: applications of evolutionary computing, LNCS, vol 3907, pp 788-799
23. Yang S, Yao X (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Trans Evol Comput 12(5):542-561