Combating user fatigue in iGAs: Partial ordering, support vector machines, and synthetic fitness

GECCO 2005 - Genetic and Evolutionary Computation Conference

Volumn , Issue , 2005, Pages 1363-1370

  Llorà, Xavier ^a   Sastry, Kumara ^a   Goldberg, David E ^a   Gupta, Abhimanyu ^a   Lakshmi, Lalitha ^a  

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

Author keywords

Human Evaluation; Interactive Evolutionary Computation; Support Vector Machines; Synthetic Fitness

Indexed keywords

INTERACTIVE COMPUTER SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION; PROBLEM SOLVING;

HUMAN EVALUATION; INTERACTIVE EVOLUTIONARY COMPUTATION; SUPPORT VECTOR MACHINES; SYNTHETIC FITNESS;

GENETIC ALGORITHMS;

EID: 32444431516     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. T. Bäck. Selective pressure in evolutionary algorithms: A characterization of selection mechanisms. Proceedings of the First IEEE Conference on Evolutionary Computation, pages 57-62, 1994.
2. T. Bäck. Generalized convergence models for tournament - and (μ, λ) - selection. Proceedings of the Sixth International Conference on Genetic Algorithms, pages 2-8, 1995.
3. M. G. Bulmer. The Mathematical Theory of Quantitative Genetics. Oxford University Press, Oxford, 1985.
4. C. Caldwell and V. S. Johnston. Tracking a criminal suspect through face-space with a genetic algorithm. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 416-421. Morgan Kaufmann, 1991.
5. C. A. Coello-Coello. An updated survey of GA-Based Multiobjective Optimization Techniques. Technical report lania-rd-09-08, Laboratorio Nacional de Informática Avanzada (LANIA), Xalapa, Veracruz, México, December, 1998.
6. N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines. Cambridge Press, 2000.
7. R. Dawkins. The blind watchmaker. New York: W. W. Norton, 1986.
8. K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan. A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. KanGAL report 200001, Indian Institute of Technology, 2000.
9. D. E. Goldberg. Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley, 1989.
10. D. E. Goldberg. The design of innovation: Lessons from and for competent genetic algorithms. Kluwer Academic Publisher, 2002.
11. D. E. Goldberg, K. Deb, and J. H. Clark. Genetic algorithms, noise, and the sizing of populations. Complex Systems, 6:333-362, 1992. (Also IlliGAL Report No. 91010).
12. D. E. Goldberg, B. Korb, and K. Deb. Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems, 3(5):493-530, 1989.
13. G. Harik. Linkage Learning via Probabilistic Modeling in the ECGA. IlliGAL Report No. 99010, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 1999.
14. G. Harik, E. Cantú-Paz, D. E. Goldberg, and B. L. Miller. The gambler's ruin problem, genetic algorithms, and the sizing of populations. Evolutionary Computation, 7(3):231-253, 1999. (Also IlliGAL Report No. 96004).
15. G. Harik, F. Lobo, and D. E. Goldberg. The compact genetic algorithm. Proceedings of the IEEE International Conference on Evolutionary Computation, pages 523-528, 1998. (Also IlliGAL Report No. 97006).
16. J. H. Holland. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press/Bradford Books edition, 1975.
17. P. Larrañaga and J. A. Lozano, editors. Estimation of Distribution Algorithms. Kluwer Academic Publishers, Boston, MA, 2002.
18. B. L. Miller. Noise, Sampling, and Efficient Genetic Algorithms. PhD thesis, University of Illinois at Urbana-Champaign, General Engineering Department, Urbana, IL, May 1997. (Also IlliGAL Report No. 97001).
19. B. L. Miller and D. E. Goldberg. Genetic algorithms, tournament selection, and the effects of noise. Complex Systems, 9(3):193-212, 1995. (Also IlliGAL Report No. 95006).
20. H. Mühlenbein and D. Schlierkamp-Voosen. Predictive models for the breeder genetic algorithm: I. continous parameter optimization. Evolutionary Computation, 1(1):25-49, 1993.
21. V. Pareto. Cours d'Economie Politique, volume I and II. F. Rouge, Lausanne, 1896.
22. M. Pelikan, F. Lobo, and D. E. Goldberg. A survey of optimization by building and using probabilistic models. Computational Optimization and Applications, 21:5-20, 2002. (Also IlliGAL Report No. 99018).
23. K. Sastry. Evaluation-relaxation schemes for genetic and evolutionary algorithms. Master's thesis, University of Illinois at Urbana-Champaign. General Engineering Department, Urbana, IL, 2001. (
24. Also IlliGAL Report No. 2002004).
25. K. Sastry, D. E. Goldberg, and M. Pelikan. Efficiency Enhancement of Probabilistic Model Building Genetic Algorithms. IlliGAL Report No. 2004020, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL, 2004.
26. J. Shawe-Taylor and N. Cristianini. Kernel Methods for Patter Analysis. Cambridge Press, 2004.
27. H. Takagi. Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 89(9):1275-1296, 2001.
28. D. Thierens and D. E. Goldberg. Mixing in Genetic Algorithms. Proceedings of the Fifth International Conference in Genetic Algorithms, pages 38-45, 1993.
29. D. Thierens and D. E. Goldberg. Convergence models of genetic algorithm selection schemes. Parallel Problem Solving from Nature, 3:116-121, 1994.