Heterogeneous cooperative coevolution: Strategies of integration between GP and GA

GECCO 2006 - Genetic and Evolutionary Computation Conference

Volumn 1, Issue , 2006, Pages 361-368

  Vanneschi, Leonardo ^a   Mauri, Giancarlo ^a   Valsecchi, Andrea ^a   Cagnoni, Stefane ^b  

^a UNIVERSITY OF MILANO BICOCCA   (Italy)

^b UNIVERSITY OF PARMA   (Italy)

Author keywords

Coevolution; Genetic Algorithms; Genetic Programming

Indexed keywords

BIOMEDICAL ENGINEERING; CODES (SYMBOLS); COMPUTATIONAL METHODS; OPTIMIZATION; PROBLEM SOLVING; REGRESSION ANALYSIS;

BIOMEDICAL APPLICATION; COEVOLUTION; GENETIC PROGRAMMING; OPTIMIZATION PROBLEMS;

GENETIC ALGORITHMS;

EID: 33750244860     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. T. Bäck. Evolutionary Algorithms in Theory and Practice. Oxford University Press, Oxford, 1996.
2. R. K. Belew, J. Mclnerney, and N. N. Schraudolph. Evolving networks: using the genetic algorithm with connectionist learning. In Proc. of the 2nd Artificial Life Conference, pages 511-547, 1991.
3. S. Cagnoni, D. Rivero, and L. Vanneschi. A purely evolutionary memetic algorithm as a first step towards symbiotic coevolution. In Proc. of the IEEE Congress on Evolutionary Computation (CEC 2005), pages 1156 - 1163, 2005.
4. J. M. Daida, R. Bertram, S. Stanhope, J. Khoo, S. Chaudhary, and O. Chaudhary. What makes a problem GP-hard? Analysis of a tunably difficult problem in genetic programming. Genetic Programming and Evolvable Machines, 2:165-191, 2001.
5. A. Eiben and M. Jelasity. A critical note on experimental research methodology in EC. In Proc. of the Congress on Evolutionary Computation (CEC 2002), pages 582-587, 2002.
6. F. Fernández, M. Tomassini, and L. Vanneschi. An empirical study of multipopulation genetic programming. Genetic Programming and Evolvable Machines, 4(1):21-52, 2003.
7. D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.
8. F. Gomez, D. Burger, and R. Miikkulainen. A neuro-evolutipn method for dynamic resource allocation on a chip multiprocessor. In Proc. of the International Joint Conference on Neural Networks (IJCNN 2001), volume 4, pages 2355-2360, 2001.
9. J. H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, 1975.
10. M. Keijzer. Improving symbolic regression with interval arithmetic and linear scaling. In Genetic Programming, Proceedings of the 6th European Conference, EuroGP 2003, volume 2610 of LNCS, pages 71-83. Springer, 2003.
11. J. R. Koza. Genetic evolution and co-evolution of computer programs. In C. Taylor, C. Langton, J. D. Farmer, and S. Rasmussen, editors, Artificial Life II, volume X, pages 603-629. Addison-Wesley, 1991.
12. J. R. Koza. Genetic Programming. The MIT Press, Cambridge, Massachusetts, 1992.
13. S. Luke and L. Panait. Is the perfect the enemy of the good? In Proc. of the Genetic and Evolutionary Computation Conference (GECCO 2002), pages 820-828, 2002.
14. D. Moriarty and R. Miikkulainen. Hierarchical evolution of neural networks. In Proc. of the IEEE World Congress on Computational Intelligence (WCCI 1998), pages 428-433, 1998.
15. S. Nolfi, D. Parisi, and J. L. Elman. Learning and evolution in neural networks. Adaptive Behavior, 3(1):5-28, 1994.
16. E. Papaleo, P. Fantucci, M. Vai, and L. De Gioia. Three-dimensional structure of the catalytic domain of the yeast β-(1,3)-glucan transferase gas: a molecular modeling investigation. Journal of Molecular Modeling, 12(2):237-248, 2005.
17. J. Paredis. Coevolutionary computation. Artificial Life, 2(4):355-375, 1995.
18. M. Potter and K. De Jong. A cooperative coevolutionary approach to function optimization. In Proc. 3rd Conf. on Parallel Problem Solving from Nature (PPSN III), volume 866 of LNCS, pages 249-257. Springer, 1994.
19. M. Roberts and E. Claridge. Cooperative convolution of image feature construction and object detection. In Proc. 8th Conf. on Parallel Problem Solving from Nature (PPSN VIII), volume 3242 of LNCS, pages 902-911. Springer, 2004.
20. M. Roberts and E. Claridge. A multistage approach to cooperatively, coevolving feature construction and object detection. In Applications of Evolutionary Computing, EvoWorkshops 2005, volume 3449 of LNCS, pages 396-406. Springer, 2005.
21. S. M. Ross. Introduction to Probability and Statistics for Engineers and scientists. Academic Press, 2000.
22. C. Ryan and M. Keijzer. Ah analysis of diversity of constants of genetic programming. In Genetic Programming, 6th European Conference, EuroGP2003, volume 2610 of LNCS, pages 409-418. Springer, 2003.
23. A. Topchy and W. F. Punch. Faster genetic programming based on local gradient search of numeric leaf values. In Proc. of the Genetic and Evolutionary Computation Conference (GECCO 2001), pages 155-162, 2001.
24. A. Valsecchi. Algoritmi coevolutivi per problemi di regressione. Master's thesis, Università di Milano-Bicocca, Milano, 2005.
25. L. Vanneschi. Theory and Practice for Efficient Genetic Programming. Ph.D. thesis, Faculty of Science, University of Lausanne, Switzerland, 2004.
26. R. P. Wiegand. An Analysis of Cooperative Coevolutionary Algorithms. Ph.D. thesis, George Mason University, Fairfax, VA, USA, 2004.
27. B. T. Zhang and H. Mühlenbein. Evolving optimal neural networks using genetic algorithms with Occam's razor. Complex Systems, 7(3): 199-220, 1993.