The roles of diversity preservation and mutation in preventing population collapse in multiobjective genetic programming

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 1551-1558

  Badran, Khaled M S ^a   Rockett, Peter I ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Bloat control; Diversity preservation; Genetic programming; Multiobjective optimization; Population collapse

Indexed keywords

GENETIC ALGORITHMS; LEARNING SYSTEMS; MULTIOBJECTIVE OPTIMIZATION;

BLOAT CONTROL; DIVERSITY PRESERVATION; MUTATION OPERATORS; POPULATION COLLAPSE;

GENETIC PROGRAMMING;

EID: 34548100305     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277272     Document Type: Conference Paper

References (14)

1. C. L. Blake and C. J. Merz. UCI Repository of Machine Learning Databases, http: //www.ics.uci.edu/~mlearn/MLRepository.html, 1998.
2. S. Bleuler, M. Brack, L. Theile, and E. Zitzler. Multiobjective genetic programming: Reducing bloat using SPEA2. In Congress on Evolutionary Computation, pages 536-543, Seoul, Korea, 2001. IEEE.
3. E. D. de Jong and J. B. Pollack. Multi-objective methods for tree size control. Genetic Programming and Evolvable Machines, 4(3):211-233, 2003.
4. A. Ekárt and S. Z. Németh. Selection based on the Pareto nondomination criterion for controlling code growth in genetic programming. Genetic Programming and Evolvable Machines, 2(1):61-73, 2001.
5. P. J. Fleming. Personal communication, 2007.
6. th International Conference of Genetic Algorithms, pages 416-423, San Mateo, CA, 1993. Morgan Kaufmann.
7. st European Workshop on Genetic Programming, pages 14-15, Paris, France, 1998. Springer-Verlag.
8. rd European Conference on Genetic Programming (EuroGP'2000), pages 304-315, Edinburgh, 2000. Springer-Verlag.
9. st European Workshop on Genetic Programming, pages 37-48, Paris, France, 1998. Springer-Verlag.
10. T.-S. Lim, W.-Y. Loh, and Y.-S. Shih. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40(3): 203-229, 2000.
11. R. Poli and W. B. Langdon. Genetic programming with one-point crossover and point mutation. Technical Report CSRP-97-13, Department of Computer Science, University of Birmingham, Birmingham, UK, 1997.
12. K. Rodríguez-Vázquez, C. M. Fonseca, and P. J. Fleming. Identifying the structure of non-linear dynamic systems using multiobjective genetic programming. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans, 34(4):531-547, 2004.
13. Y. Zhang and P. I. Rockett. Evolving optimal feature extraction using multi-objective genetic programming: A methodology and preliminary study on edge detection. In H.-G. Beyer, U.-M. O'Reilly, D. V. Arnold, W. Banzhaf, C. Blum, E. W. Bonabeaum, E. Cantu-Paz, D. Dasgupta, K. Deb, J. A. Foster, E. D. de Jong, H. Lipson, X. Llora, S. Mancoridis, M. Pelikan, G. R. Raidl, T. Soule, A. M. Tyrrell, J.-P. Watson, and E. Zitzler, editors, Genetic and Evolutionary Computation Conference (GECCO 2005), pages 795-802, Washington, DC, 2005. ACM Press.
14. Y. Zhang and P. I. Rockett. Feature extraction using multi-objective genetic programming. In Y. Jin, editor, Multi-Objective Machine Learning. Springer, Heidelberg, 2006.