On the deleterious effects of a priori objectives on evolution and representation

Genetic and Evolutionary Computation Conference, GECCO'11

Volumn , Issue , 2011, Pages 957-964

  Woolley, Brian G ^a   Stanley, Kenneth O ^a  

^a University of Central Florida   (United States)

Author keywords

Deception; Fitness; Indirect encoding; Non objective search; Representations; Stepping stones

Indexed keywords

DECEPTION; FITNESS; INDIRECT ENCODING; NON-OBJECTIVE SEARCH; REPRESENTATIONS; STEPPING STONE;

BIOLOGY; CALCULATIONS; EXPERIMENTS;

EVOLUTIONARY ALGORITHMS;

EID: 84860407590     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2001576.2001707     Document Type: Conference Paper

References (28)

1. R. Dawkins. The Blind Watchmaker. Longman, Essex, U.K., 1986.
2. K. A. De Jong. Evolutionary Computation: A Unified Perspective. MIT Press, Cambridge, MA, 2002.
3. S. Droste, T. Jansen, and I. Wegener. On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science, 276(1-2):51-81, 2002.
4. A. Eiben and J. Smith. Introduction to evolutionary computing. Springer-Verlag, 2003.
5. S. Ficici and J. Pollack. Challenges in coevolutionary learning: Arms-race dynamics, open-endedness, and mediocre stable states. Artificial life VI, page 238, 1998.
6. D. Floreano, P. Dürr, and C. Mattiussi. Neuroevolution: from architectures to learning. Evolutionary Intelligence, 1:47-62, 2008.
7. D. B. Fogel. Evolutionary computation: toward a new philosophy of machine intelligence, third edition. Wiley-IEEE Press, 2006.
8. D. E. Goldberg. Genetic algorithms in search, optimization, and machine learning. Addison-wesley, 1989.
9. D. E. Goldberg and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. In J. J. Grefenstette, editor, Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application, pages 41-49, Hillsdale, NJ, 1987. L. Erlbaum Associates Inc.
10. F. Gomez and R. Miikkulainen. Incremental evolution of complex general behavior. Adaptive Behavior, 5:317-342, 1997.
11. F. J. Gomez, J. Schmidhuber, and R. Miikkulainen. Efficient non-linear control through neuroevolution. In J. Fürnkranz, T. Scheffer, and M. Spiliopoulou, editors, Proceedings of the 17th European Conference on Machine Learning, volume 4212 of Lecture Notes in Computer Science, pages 654-662. Springer, 2006. (Pubitemid 44618874)
12. D. James and P. Tucker. ANJI homepage. http://anji.sourceforge.net, 2004.
13. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, 1992.
14. J. Lehman and K. O. Stanley. Exploiting open-endedness to solve problems through the search for novelty. In S. Bullock, J. Noble, R. Watson, and M. Bedau, editors, Proceedings of the Eleventh International Conference on Artificial Life (Alife XI), Cambridge, MA, 2008. MIT Press.
15. J. Lehman and K. O. Stanley. Abandoning objectives: Evolution through the search for novelty alone. Evolutionary Computation, 2011. To appear.
16. G. E. Liepins and M. D. Vose. Deceptiveness and genetic algorithm dynamics. Technical Report CONF-9007175-1, Oak Ridge National Lab., TN; Tennessee Univ., Knoxville, TN, 1990.
17. H. Mühlenbein and T. Manning. Convergence theory and applications of the Factorized Distribution Algorithm. Journal of Computing and Information Technology, 7(1):19-32, 1999.
18. M. Pelikan and D. Goldberg. Escaping hierarchical traps with competent genetic algorithms. Technical Report 2001003, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL, 2001.
19. A. L. Ralescu. Generalization of the Hamming distance using fuzzy sets. Technical Report JSPS Senior Research Fellowship, Laboratory for Mathematical Neuroscience, The Brain Science Institute, RIKEN, Japan, 2003.
20. N. Saravanan and D. B. Fogel. Evolving neural control systems. IEEE Expert, pages 23-27, June 1995.
21. J. Secretan, N. Beato, D. B. D. Ambrosio, A. Rodriguez, A. Campbell, J. T. Folsom-Kovarik, and K. O. Stanley. Picbreeder: A case study in collaborative evolutionary exploration of design space. Evolutionary Computation, 2011. To appear.
22. J. Secretan, N. Beato, D. B. D'Ambrosio, A. Rodriguez, A. Campbell, and K. O. Stanley. Picbreeder: Evolving pictures collaboratively online. In CHI '08: Proceedings of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, pages 1759-1768, New York, NY, 2008. ACM.
23. K. O. Stanley. Compositional pattern producing networks: A novel abstraction of development. Genetic Programming and Evolvable Machines Special Issue on Developmental Systems, 8(2):131-162, 2007. (Pubitemid 46904474)
24. K. O. Stanley and R. Miikkulainen. Evolving neural networks through augmenting topologies. Evolutionary Computation, 10:99-127, 2002.
25. K. O. Stanley and R. Miikkulainen. Competitive coevolution through evolutionary complexification. Journal of Artificial Intelligence Research, 21:63-100, 2004. (Pubitemid 41525898)
26. H. Takagi. Interactive evolutionary computation: Fusion of the capacities of EC optimization and human evaluation. Proceedings of the IEEE, 89(9):1275-1296, 2001. (Pubitemid 33766623)
27. S. Whiteson and P. Stone. Evolutionary function approximation for reinforcement learning. Journal of Machine Learning Research, 7:877-917, 2006. (Pubitemid 43736560)
28. N. Zaera, D. Cliff, and J. Bruten. (Not) evolving collective behaviours in synthetic fish. In P. Maes, M. J. Mataric, J. A. Meyer, J. Pollack, and S. W. Wilson, editors, Proceedings of the fourth International Conference on Simulation of Adaptive Behavior (SAB'96), pages 635-642, Cambridge, Mass., 1996. MIT Press.