Wise breeding GA via machine learning techniques for function optimization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2723, Issue , 2003, Pages 1172-1183

  Llorà, Xavier ^a   Goldberg, David E ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CALCULATIONS; DECISION TREES; LEARNING SYSTEMS;

BLACK-BOX FUNCTIONS; EVOLUTIONARY PROCESS; FITNESS FUNCTIONS; FUNCTION OPTIMIZATION; INDUCTIVE LEARNING; INDUCTIVE MACHINE LEARNING; MACHINE LEARNING TECHNIQUES; STATISTICAL LEARNING;

EVOLUTIONARY ALGORITHMS;

EID: 35248897726     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45105-6_125     Document Type: Article

References (22)

1. Baluja, S.: Population-based incremental learning: A method for integrating genetic search function optimization and competitive learning. Technical Report CMU-CS-94-163, Carnegie Mellon University (1994)
2. Baluja, S.: An empirical comparation of seven iterative and evolutionary function optimization heuristics. Technical Report CMU-CS-95-193, Carnegie Mellon University (1995)
3. Juels, A., Wattenberg, M.: Stochastic hillclimbing as a baseline method for evaluating genetic algorithms. Technical Report CSD-94-834, Computers Science Department, University of California at Berkeley, USA (1995)
4. Mühlenbein, H., Paaß, G.: From recombination of genes to estimation of distributions I. In: Lecture Notes in Computer Science 1411: Parallel Problem Solving from Nature - PPSN IV, Springer (1996) 178-187
5. Larrañaga, P., Lozano, J.: Estimation of Distribution Algorithms. GENA 2. Kluwer Academic Publishers (2002)
6. Mühlenbein, H.: The equation for response to selection and its use for prediction. Evolutionary Computation 5 (1998) 303-346
7. Harik, G., Lobo, F., Goldberg, D.: The compact genetic algorithm. In: Proceedings of the IEEE Conferrence on Evolutionary Computation, IEEE press (1998) 523-528
8. Pelikan, M.: Bayesian Optimization Algorithm: from single level to hierarchy. PhD thesis, University of Illinois at Urbana Champaign, Urbana, Illinois, USA (June, 2002)
9. Pelikan, M., Goldberg, D., Cantú-Paz, E.: BOA: the Bayesian Optimization Algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), Morgan Kaufmann (1999) 525-532
10. Michalski, R.: Learnable Evolution: Combining Symbolic and Evolutionary Learning. In: Proceedings of the 4th International Workshop on Multistrategy Learning, Decenzano del Garda, Italy. (June 11-14, 1998)
11. Michalski, R.: Learnable Evolution Model: Evolutionary Processes Guided by Machine Learning. Machine Learning 38 (2000) 9-40
12. Wnek, J., Kaufman, K., Bloedorn, E., Michalski, R.: Inductive Learning System AQ15c: The Method and User's Guide. Reports of the Machine Learning and Inference Laboratory, MLI 95-4, George Manson University, Fairfax, VA (March, 1995)
13. Kaufman, K., Michalski, R.: The AQ18 Machine Learning and Data Mining System: An Implementation and User's Guide. Reports of the Machine Learning and Inference Laboratory, George Manson University, Fairfax, VA (1999)
14. Quinlan, J.R.: Induction of decision trees. Machine Learning 1 (1986) 81-106
15. Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann (1993)
16. Baluja, S., Caruana, R.: Removing the genetics from standard genetic algorithms. In: International Conference on Machine Learning, Morgan Kaufmann (1995) 38-46
17. Mitchell, T.M.: Machine Learning. McGraw Hill (1997)
18. Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers (2002)
19. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press/Bradford Books edition (1975)
20. Gonzalez, C., Lozano, J., Larranarraga, P.: Analyzing the pbil algorithm by means of discrete dynamical systems. Complex Systems 12 (2001) 465-479
21. Haussler, D.: Quantifying inductive bias: AI learning algorithms and Valiant's learning framework. Artificial Intelligence 36 (1988) 477-221
22. Valiant, L.: A theory of the learnable. Communications of ACM 27 (1984) 1134-1142