A fast evaluation strategy for evolutionary algorithms

Applied Soft Computing Journal

Volumn 2, Issue 3, 2003, Pages 156-173

  Salami, Mehrdad ^a   Hendtlass, Tim ^a  

^a SWINBURNE UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

Evolutionary algorithm; Fast evolutionary algorithm; Fitness evaluation; Image compression

Indexed keywords

COMPUTATIONAL METHODS; ERROR COMPENSATION; FUNCTIONS; IMAGE COMPRESSION; OPTIMIZATION; PROBLEM SOLVING; RANDOM PROCESSES; RELIABILITY;

FAST EVOLUTIONARY ALGORITHMS; FITNESS EVALUATION; OBJECTIVE FUNCTIONS;

EVOLUTIONARY ALGORITHMS;

EID: 4344607325     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1568-4946(02)00067-4     Document Type: Article

References (23)

1. J. Holland, Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, MA, 1975.
2. L.J. Fogel, A.J. Owens, M.J Walsh, Artificial Intelligence Through Simulated Evolution, Wiley, New York, 1966.
3. J.J. Grefenstette, Optimization of control parameters for genetic algorithms, IEEE Trans. System Man Cybernetics SMC 16 (1) (1986) 122-128.
4. D.E. Goldberg, K. Deb, D. Thierens, Toward a better understanding of mixing in genetic algorithm, J. Soc. Instrum. Control Eng. 32 (1) (1993) 10-16.
5. M. Salami, H. Sakanashi, M. Iwata, T. Higuchi, On-Line compression of high precision printer images by evolvable hardware, in: Proceedings of the 1998 Data Compression Conference '98, IEEE Computer Society Press, Los Alamitos, CA, USA, 1998, pp. 219-228.
6. S.K. Pal, P.P. Wang, Genetic Algorithms and Pattern Recognition, CRC Press, Florida, 1996.
7. J. Holland, J.S. Reitman, Cognitive systems based on adaptive algorithms, in: D.A. Waterman, F. Hayes-Roth (Eds.), Pattern Directed Interference Systems, New York, Academic Press, 1978, pp. 313-329.
8. R. Smith, B. Dike, S. Stegmann, Fitness inheritance in genetic algorithms, in: Proceedings of the 1995 ACM Symposium on Applied Computing, ACM Press, New York, NY, 1995, pp. 345-350.
9. K. Sastry, D.E. Goldberg, M. Pelikan, Don't evaluate, inherit, IlliGAL Technical Report No. 2001013, University of Illinois at Urbana-Champaign, Urbana, Illinois, 2001.
10. M.J.D. Powell, A direct search optimization method that models the objective and constraint functions by linear interpolation, in: Proceedings of the Sixth Workshop on Optimization and Numerical Analysis, Kluwer Academic Publishers, Dordrecht, Netherlands, 1994, pp. 51-67.
11. M. El-Beltagy, A. Keane, Using self organizing maps and genetic algorithms for model selection in multilevel optimization, in: Proceedings of the 12th International Conference on Industrial Applications and Intelligence and Expert Systems, Lecture Notes in Computer Science 1611, Springer-Verlag, Berlin, 1999, pp. 137-144.
12. A. Ratle, Optimal sampling strategies for learning a fitness model, in: Proceedings of the 1999 Congress on Evolutionary Computation (CEC99), vol. 3, IEEE Press, 1999, pp.2078-2085.
13. Y. Jin, M. Olhofer, B. Sendhoff, On evolutionary optimisation with approximate fitness functions, in: Proceedings of the Genetic and Evolutionary Computation Conference GECCO, Morgan Kaufmann, San Francisco, CA, USA, 2000, pp. 786-793.
14. T. Bäck, D.B. Fogel, Z. Michalewicz, Handbook of Evolutionary Computation, Oxford University Press, Oxford, 1997.
15. J.M. Fitzpatrick, J.J. Grefenstette, Genetic algorithm in noisy environment, Machine Learning 3 (1998) 101-120.
16. U. Hammel, T. Bäck, Evolution strategies on noisy functions: how to improve convergence properties, in: Proceedings of the Parallel Problem Solving from Nature (PPSN III), Lecture Notes in Computer Science, vol. 866, Springer-Verlag, Berlin, 1994, pp. 159-168.
17. J.J. Grefenstette, Robot learning with parallel genetic algorithms on network computers, in: Proceedings of the 1995 Summer Computer Simulation Conference SCSC'95, The Society for Computer Simulation, Ottawa, CA, 1995, pp. 352-357.
18. J.E. Baker, Reducing bias and inefficiency in the selection algorithm, in: Proceedings of the Second International Conference on Genetic Algorithm, Lawrence Erlbaum Associates, Hillsdale, NJ, 1987, pp. 14-21.
19. M. Salami, T. Hendtlass, A fitness estimation strategy for genetic algorithms, in: Proceedings of the Fifteenth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE-2002), Lecture Notes in Computer Science, vol. 2358, Springer-Verlag, Berlin, Germany, 2002, pp. 502-513.
20. M. Salami, T. Hendtlass, A fast evolutionary algorithm for image compression in hardware, in: Proceedings of the Fifteenth International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE-2002), Lecture Notes in Computer Science, vol. 2358, Springer-Verlag, Berlin, Germany, 2002, pp. 241-252.
21. J. Marin, R.V. Sole, Macroevolutionary algorithms: a new optimization method on fitness landscape, IEEE Trans. Evol. Comput. 3 (4) (1999) 272-286.
22. A.E. Eiben, T. Bäck, An empirical investigation of multi-parent recombination operators in evolution strategies, Evol. Comput. 5 (3) (1997) 347-365.
23. M.J. Weinberger, G. Seroussi, G. Sapiro, LOCO-I: a low complexity, context-based, lossless image compression algorithm, in: Proceedings of the 1996 Data Compression Conference, IEEE Computer Society Press, Los Alamitos, CA, USA, 1996, pp. 140-149.