A general cost-benefit-based adaptation framework for multimeme algorithms

Memetic Computing

Volumn 2, Issue 3, 2010, Pages 201-218

  Jakob, Wilfried ^a  

^a KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

Author keywords

Adaptation; Continuous optimisation; Memetic algorithms; Multimeme algorithms; Strategy parameter reduction

Indexed keywords

ADAPTATION; MEMETIC ALGORITHMS; MULTIMEME ALGORITHMS; OPTIMISATIONS; STRATEGY PARAMETER REDUCTION;

COST EFFECTIVENESS; OPTIMIZATION; PARAMETER ESTIMATION;

EVOLUTIONARY ALGORITHMS;

EID: 77956683172     PISSN: 18659284     EISSN: 18659292     Source Type: Journal    
DOI: 10.1007/s12293-010-0040-9     Document Type: Article

References (58)

1. Auger A, Hansen N (2005) Performance evaluation of an advanced local search evolutionary algorithm. In: Conference proceedings of IEEE congress on evolutionary computation, CEC 2005, pp 1777-1784.
2. Auger A, Hansen N (2005) A restart CMA evolution strategy with increasing population size. In: Conference proceedings of IEEE congress on evolutionary computation, CEC 2005, pp 1769-1776.
3. Bader-El-Den M, Poli R, Fatima S (2009) Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework. J Memet Comput 1(3): 205-219.
4. Bäck T (1992) GENEsYs 1. 0. http://www. cs. cmu. edu/afs/cs/project/ai-repository/ai/areas/genetic/ga/systems/genesys/0. html. Accessed 11 Aug 2009.
5. Bambha NK, Bhattacharyya SS, Zitzler E, Teich J (2004) Systematic integration of parameterized local search into evolutionary algorithms. IEEE Trans Evolut Comput 8(2): 137-155.
6. Blume C (1991) GLEAM-a system for intuitive learning. In: Schwefel H-P, Männer R (eds) Conference proceedings of PPSN I, LNCS 496. Springer, Berlin, pp 48-54.
7. Blume C, Gerbe M (1994) Deutliche Senkung der Produktionskosten durch Optimierung des Ressourceneinsatzes. In: atp, vol 36(5), Oldenbourg Verlag, München, pp 25-29 (in German).
8. Blume C, Jakob W (2002) GLEAM-an evolutionary algorithm for planning and control based on evolution strategy. In: Cantú-Paz E (ed) Conference proceedings of GECCO 2002, LBP, pp 31-38.
9. Blume C, Jakob W (2009) GLEAM - General Learning Evolutionary Algorithm and Method: ein Evolutionärer Algorithmus und seine Anwendungen. (in German), vol. 32 of Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik. KIT Scientific Publishing, Karlsruhe.
10. Box MJ (1965) A new method of constrained optimization and a comparison with other methods. Comput J 8: 42-52.
11. Burke E, Kendall G, Newall J, Hart E, Ross P, Schulenberg S (2003) Hyper-heuristics: an emerging direction in modern search technology. In: Glover F, Kochenberg GA (eds) Handbook of metaheuristics. Kluwer, Dordrecht, pp 457-474.
12. Caponio A, Cascella GL, Neri F, Salvatore N, Sumner M (2007) Fast adaptive memetic algorithm for online and offline control design of PMSM drives. IEEE Trans Syst Man Cybern Part B 37(1): 28-41.
13. Davis, L (eds) (1991) Handbook of genetic algorithms. van Nostrand Reinhold, NY.
14. Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evolut Comput 3(2): 124-141.
15. Gruau F, Whitley D (1993) Adding learning to the cellular development of neural networks: evolution and the Baldwin effect. Evolut Comput 1(3): 213-233.
16. Gorges-Schleuter M (1990) Genetic algorithms and population structures-a massively parallel algorithm. Dissertation, Department of Computer Science, University of Dortmund, Germany.
17. Gorges-Schleuter M (1998) A comparative study of global and local selection in evolution strategies. In: Eiben AE, Bäck T, Schoenauer M, Schwefel H-P (eds) Conference proceedings of PPSN V, LNCS 1498. Springer, Berlin, pp 367-377.
18. Gorges-Schleuter M et al (1999) An analysis of local selection in evolution strategies. In: Banzhaf W (eds) Conference proceedings of GECCO 1999. Morgan Kaufmann, San Francisco, pp 847-854.
19. Hart WE (1994) Adaptive global optimization with local search. Dissertation, University of California, USA.
20. Hart WE, Krasnogor N, Smith JE (2004) Editorial introduction. Evolut Comput 12(3): v-vi (special issue on Memetic Algorithms).
21. Hart WE, Krasnogor N, Smith JE (eds) (2004) Recent advances in memetic algorithms. In: Studies in fuzziness and soft computing, vol 166, Springer.
22. Hansen B (2006) Compilation of results on the 2005 CEC benchmark function set. CoLab, Institute of Computational Science, ETH Zurich, Technical Report. http://www3. ntu. edu. sg/home/epnsugan/index_fileq/CEC-05/compareresults. pdf. Accessed 18 Aug 2009.
23. Jakob W et al (2002) HyGLEAM-an approach to generally applicable hybridization of evolutionary algorithms. In: Merelo JJ (eds) Conference proceedings of PPSN VII, LNCS 2439. Springer, Berlin, pp 527-536.
24. Jakob W, Blume C, Bretthauer G (2004) Towards a generally applicable self-adapting hybridization of evolutionary algorithms. In: Conference proceedings of GECCO 2004, LNCS 3102, Springer, Berlin, pp 790-791 and LBP.
25. Jakob W et al (2006) Towards an adaptive multimeme algorithm for parameter optimisation suiting the engineers' needs. In: Runarsson TP (eds) Conference proceedings of PPSN IX, LNCS 4193. Springer, Berlin, pp 132-141.
26. Jakob W et al (2008) A cost-benefit-based adaptation scheme for multimeme algorithms. In: Jakob W (eds) Conference proceedings of parallel processing and applied mathematics 2007. Springer, LNCS 4967, pp 509-519.
27. Jakob W, Quinte A, Stucky K-U, Süß W (2008) Fast multi-objective scheduling of jobs to constrained resources using a hybrid evolutionary algorithm. In: Rudolph G (eds) Conference proceedings of PPSN X, LNCS 5199. Springer, Berlin, pp 1031-1040.
28. Krasnogor N, Smith JE (2001) Emergence of profitable search strategies based on a simple inheritance algorithm. In: Conference proceedings of GECCO 2001, M. Kaufmann, San Francisco, pp 432-439.
29. Krasnogor N, Blackburne BP, Burke EK, Hirst JD (2002) Multimeme algorithms for protein structure prediction. In: Conference proceedings of PPSN VII, LNCS 2439, Springer, Berlin, pp 769-778, citation: p 772.
30. Krasnogor N (2002) Studies on the theory and design space of memetic algorithms. Dissertation, University West of England, UK.
31. Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans Evolut Comput 5(9): 474-488.
32. Law NL, Szeto KY (2007) Adaptive genetic algorithm with mutation and crossover matrices. In: Veloso MM (ed) Conference proceedings of IJCAI-07, pp 2330-2333.
33. Land MWS (1998) Evolutionary algorithms with local search for combinatorial optimization. Dissertation, University of California, USA.
34. Lozano M, Herrera F, Krasnogor N, Molina D (2004) Real-coded memetic algorithms with crossover hill-climbing. Evolut Comput 12(2): 273-302.
35. Meuth R, Lim MH, Ong YS, Wunsch DC II (2009) A proposition on memes and meta-memes in computing for high-order learning. J Memet Comput 1(2): 85-100.
36. Nelder JA, Mead RA (1965) Simplex method for function minimization. Comput J 7: 308-313.
37. Neri F, Tirronen V, Kärkkäinen T, Rossi T (2007) Fitness diversity based adaptation in multimeme algorithms: a comparative study. In: Conference proceedings of IEEE congress on evolutionary computing, CEC 2007, pp 2374-2381.
38. Neri F, Toivanen J, Cascella GL, Ong YS (2007) An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Trans Comput Biol Bioinform 4(2): 264-278.
39. Neri F, Tirronen V (2008) On memetic differential evolution frameworks: a study of advantages and limitations in hybridization. In: Conference proceedings of IEEE congress on evolutionary computing, CEC 2008, pp 2135-2142.
40. Neri F, Tirronen V (2009) Scale factor local search in differential evolution. J Memet Comput 1(2): 153-171.
41. Nguyen QH, Ong YS, Krasnogor N (2007) A study on design issues of memetic algorithm. In: Conference proceedings of IEEE congress on evolutionary computing, CEC 2007, pp 2390-2397.
42. Noman N, Iba H (2008) Accelerating differential evolution using an adaptive local search. IEEE Trans Evolut Comput 12(1): 107-125.
43. Ong YS, Keane AJ (2004) Meta-Lamarckian learning in memetic algorithms. IEEE Trans Evolut Comput 8(2): 99-110.
44. Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern Part B 36(1): 141-152.
45. Orvosh D, Davis L (1993) Shall we repair? Genetic algorithms, combinatorial optimization, and feasibility constraints. In: Forrest S (ed) Conference proceedings of Morgan Kaufmann, San Mateo, CA, p 650.
46. Posik P (2005) Real-parameter optimization using the mutation step co-evolution. In: Conference proceedings of IEEE congress on evolutionary computing, CEC 2005, pp 872-879.
47. Rosenbrock HH (1960) An automatic method for finding the greatest or least value of a function. Comput J 3: 175-184.
48. Schwefel H-P (1995) Evolution and optimum seeking. Wiley, New York.
49. Smith JE (2003) Co-evolving memetic algorithms: a learning approach to robust scalable optimisation. In: Conference proceedings of IEEE congress on evolutionary computing, CEC 2003, pp 498-505.
50. Smith JE et al (2008) Self-adaptation in evolutionary algorithms for combinatorial optimisation. In: Cotta C (eds) Adaptive and multilevel metaheuristics, SCI 136. Springer, Berlin, pp 31-57.
51. Sudholt D (2009) The impact of parameterization in memetic evolutionary algorithms. Theor Comput Sci. doi: 10. 1016/j. tcs. 2009. 03. 003.
52. Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y-P, Auger A, Tiwari S (2005) Problem definition and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, Nanyang Technical University, Singapore and KanGAL Technical Report #2005005, IIT Kanpur, India. Available at http://www. ntu. edu. sg/home/epnsugan.
53. Szeto KY, Zhang J (2005) Adaptive genetic algorithm and quasi-parallel genetic algorithm: application to low-dimensional physics. In: Conference proceedings of large-scale scientific computing (LSSC 2005), LNCS 3743, Springer, Berlin, pp 189-196.
54. Takahara S, Kusumoto Y, Miyamoto S (2001) An adaptive meta-heuristic approach using partial optimization to non-convex polygons allocation problem. In: Conference proceedings of fuzzy systems 2001, IEEE, vol 3, pp 1191-1194.
55. Tirronen V, Neri F (2009) Differential evolution with fitness diversity self-adaptation. In: Chiong R (eds) Nature-inspired algorithms for optimisation, SCI 193. Springer, Berlin, pp 199-234.
56. Whitley D, Gordon V, Mathias K et al (1994) Lamarckian evolution, the Baldwin effect and function optimization. In: Davidor Y (eds) Conference proceedings of PPSN III, LNCS 866. Springer, Berlin, pp 6-14.
57. Yang J-M, Kao C-Y (2000) Integrating adaptive mutations and family competition into genetic algorithms as function optimizer. Soft Comput 4(2): 89-102.
58. Zitzler E, Teich J, Bhattacharyya SS (2000) Optimizing the efficiency of parameterized local search within global search: a preliminary study. In: Conference proceedings of IEEE congress on evolutionary computing, CEC 2000, pp 365-372.