Efficiency speed-up strategies for evolutionary computation: Fundamentals and fast-GAs

Applied Mathematics and Computation

Volumn 142, Issue 2-3, 2003, Pages 341-388

  Xu, Zong Ben ^a   Leung, Kwong Sak ^b   Liang, Yong ^b   Leung, Yee ^b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Efficiency speed up; Evolutionary computation; Exclusion based selection operator; Genetic algorithms; Refined cellular partioning; Reinitialization; Space discretization; Space shrinking; Splicing decomposable representation scheme

Indexed keywords

COMPUTATION THEORY; EFFICIENCY; FRACTALS; IMAGE COMPRESSION; OPTIMIZATION;

SPACE DISCRETIZATION;

GENETIC ALGORITHMS;

EID: 0038623869     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0096-3003(02)00309-0     Document Type: Article

References (44)

1. Bäck T., Schwefel H.P. An overview of evolutionary algorithm for parameter optimization. Evolutionary Computation. 1(1):1993;1-23.
2. Bäck T., Kursawe F. Evolutionary algorithms for fuzzy logic: a brief overview. Bouchon-Meunier B., Yager R.R., Zadeh L.A. Fuzzy Logic and Soft Computing. 1995;3-10 World Scientific, Singapore.
3. Barnsley M.F., Demoko S. Iterated function systems and the global construction of fractals. Proceedings of Royal Soceity London A. 399:1985;243-275.
4. Deb K. Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evolutionary Computation. 7(3):1999;205-231.
5. Davis T.E., Principle J.C. A simulated annealing like convergence theory for the simple genetic algorithm. Proceedings of Fourth International Conference on Genetic Algorithms. 1991;174-218 Morgan Kaufmann, San Mateo.
6. Eshelman L.J., Schaffer J.D. Real-coded genetic algorithms and interval schemate. Whitley L.D. Foundations of Genetic Algorithms, II. 1993;187-202 Morgan Kaufmann, San Mateo, CA.
7. Fogel D.B. An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks. 5:1994;3-13.
8. Fogel D.B. Evolutionary Computation: Towards a New Philosophy of Machine Intelligence. 1995;IEEE Press, New York.
9. Fayyad U.M., Piatetsky-Shapire G., Smyth P., Uthurusamy R. Advances in Knowledge Discovery and Data Mining. 1996;MIT Press, Cambridge, MA.
10. Fonseca C.M., Fleming P.J. An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation. 3:1995;1-16.
11. Francois O. An evolutionary strategy for global minimization and its Markov chain analysis. IEEE Transactions on Evolutionary Computation. 2(30):1998;77-90.
12. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning. 1989;Addison-Wesley, Reading, MA.
13. Goldberg D.E., Korb B., Deb K. Messy genetic algorithm: motivation, analysis, and the first result. Complex System. 3:1989;493-530.
14. Grefenstette J.J. Optimization of control parameters for genetic algorithms. IEEE Transactions on System, Man, and Cybernetics. 16(1):1986;122-128.
15. Hansen E. Global Optimization Using Interval Analysis. 1992;Marcel Dekker, New York.
16. Holland J.H. Adaption in Natural and Artificial Systems. 1975;The university of Michigan Press, Ann Arbor.
17. Y. Hanaki, T. Hashiyama, S. Okuma, The acceleration of evolutionary computation using fitness estimation, in: Proceedings of the 1999 IEEE/ASME International Conference on Advanced Intelligent Mechtronics, Atlanta, USA, 1999, pp. 776-781.
18. Harik G.R., Lobo F.G., Goldberg D.E. The compact genetic algorithm. IEEE Transactions on Evolutionary Computation. 3:1999;287-297.
19. Houck C.R., Joines J.A., Kay M.G., Wilson J.R. Empirical investigation of the benefits of partial Lamarckiansim. Evolutionary Computation. 5(1):1997;31-60.
20. Jacquin A.E. Fractal image coding: a review. Proceedings of IEEE. 10:1993;1451-1465.
21. Koza J.R. Genetic Programming II: Automatic Discovery of Reusable Subprograms. 1994;MIT Press, Cambridge, MA.
22. G. Li, Y. Leung, Z.B. Xu, K.S. Leung, Hybrid genetic algorithms for global optimization of functions, Evolutionary Computation (in press).
23. Leung K.S., Duan Q.H., Xu Z.B., Wong C.K. A new model of simulated evolutionary computation: convergence analysis and specifications. IEEE Transactions on Evolutionary Computation. 5:2001;3-16.
24. Leung K.S., Seng J.Y., Xu Z.B. Efficiency speed-up strategies for evolutionary computation: an adaptive implementation. Engineering Computations. 19:2002;272-304.
25. Mathias K.E., Whitely L.D. Changing representation during search: a comparative study of delta coding. Evolutionary Computation. 2:1995;249-278.
26. Michelewicz Z. Genetic. Algorithm + Data Structure = Evolutionary Programs third ed. 1996;Springer-Verlag, New York.
27. Muhlenbein H., Schliekamp-Voosen D. Predictive models for the breeder genetic algorithm I: continuous parameter optimization. Evolutionary Computation. 1:1993;25-49.
28. Marin Jesus, Sole R.V. Macroevolutionary algorithms: a new optimization method on fitness landscapes. IEEE Transactions on Evolutionary Computation. 3:1999;272-286.
29. Parsons R.J., Forrest S., Burks C. Genetic algorithms, and DNA fragment assembly. Machine Learning. 21:1995;11-33.
30. Potter M.A., De Jong K.A. A cooperative coevolutionary approach to function optimization. Davidor Y., Schwefel H.-P., Manner R. Parallel Problem Solving from Nature. 1994;249-257 Springer-Verlag, Berlin.
31. Renders J.M., Flasse S.P. Hybrid method using genetic algorithms for global optimization. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics. 26(2):1996;243-258.
32. Schraudolph N.N., Belew R.K. Dynamic parameter encoding for genetic algorithms. Machine Learning. 9:1992;9-21.
33. R. Salomon, Raising theoretical questions about the utility of genetic algorithms, in: P.J. Angeline, R.G. Reynolds, J.R. McDonnell, R. Eberhart, (Eds.), Proceedings of Sixth Annual Conference on Evolutionary Programming VI, 1997, pp. 275-284.
34. Salomon R. Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions: a survey of some theoretical and practical aspects of genetic algorithms. Biological Systems. 39(3):1996;263-278.
35. Srinivas M., Patnaik L.M. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on System, Man and Cybernetics. 24(4):1994;17-26.
36. Schwefel H.P. Evolution and Optimum Seeking. 1995;John Wiley, Chichester, UK.
37. Tsutsui S., Fujimoto Y., Ghosh A. Forking genetic algorithms: GAs with search space division schemes. Evolutionary Computation. 5(1):1997;61-80.
38. Vrscay E.R. Iterated function systems: theory, applications and the inverse problem. Belair J., Dubuc S. Fractal Geometry and Analysis. 1991;405-468 Kluwer Academic Publishers, Dordrecht.
39. Wu A.S., Banzhaf W. Introduction to the special issue: variable-length representation and noncoding segments for evolutionary algorithms. Evolutionary Computation. 6:1999;4. iii-vi.
40. Xu Z.B., Zhang J.S., Wang W. A cell exclusion algorithm for finding all the solutions of a nonlinear system of equation. Applied Mathematics and Computation. 80:1996;181-208.
41. Xu Z.B., Zhang J.S., Leung Y.W. A general CDC formulation for specializing the cell exclusion algorithms of finding all zeros of vector functions. Applied Mathematics and Computation. 86(2&3):1997;235-259.
42. Yao X., Liu Y., Lin G.M. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation. 3:1999;82-97.
43. Yao X., Liu L. Towards designing artificial neural networks by evolution. Applied Mathematics and Computation. 91:1998;83-90.
44. Z.Y. Zhou, K.S. Leung, Z.B. Xu, Exclusion-based cooperative coevolution genetic algorithm, 2001 WSES International Conference on Evolutionary Computations (EC'01), 2001, 637(1)-637(6).