Internal reinforcement in a connectionist genetic programming approach

Artificial Intelligence

Volumn 120, Issue 2, 2000, Pages 165-198

  Teller, Astro ^a,b   Veloso, Manuela ^a  

^a Carnegie Mellon University   (United States)

^b BodyMedia Inc   (United States)

Author keywords

Bucket brigade; Evolutionary computation; Genetic programming; Internal reinforcement; Machine learning; Neural programming; Signal understanding

Indexed keywords

EID: 0346479554     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0004-3702(00)00023-0     Document Type: Article

References (44)

1. L. Altenberg, The evolution of evolvability in genetic programming, in: K.E. Kinnear Jr. (Ed.), Advances in Genetic Programming, MIT Press, Cambridge, MA, 1994, pp. 47-74.
2. D. Andre, A. Teller, A study in program response and the negative effects of introns in genetic programming, in: J.R. Koza, D.E. Goldberg, D.B. Fogel, R.L. Riolo (Eds.), Genetic Programming 1996: Proc. 1st Annual Conference, Stanford University, CA, MIT Press, Cambridge, MA, 1996, pp. 12-19.
3. D. Andre, Automatically defined features: The simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them, in: K.E. Kinnear Jr. (Ed.), Advances in Genetic Programming, MIT Press, Cambridge, MA, 1994, pp. 477-494.
4. D. Andre, The evolution of agents that build mental models and create simple plans using genetic programming, in: L. Eshelman (Ed.), Genetic Algorithms: Proc. 6th International Conference (ICGA95), Pittsburgh, PA, Morgan Kaufmann, San Mateo, CA, 1995, pp. 248-255.
5. P.J. Angeline, Evolutionary algorithms and emergent intelligence, Ph.D. Thesis, Ohio State University, Computer Science Department, 1993.
6. P.J. Angeline, Two self-adaptive crossover operators for genetic programming, in: P.J. Angeline, K.E. Kinnear Jr. (Eds.), Advances in Genetic Programming 2, MIT Press, Cambridge, MA, 1996.
7. P.J. Angeline, An alternative to indexed memory for evolving programs with explicit state representations, in: J.R. Koza, K. Deb, M. Dorigo, D.B. Fogel, M. Garzon, H. Iba, R.L. Riolo (Eds.), Genetic Programming 1997: Proc. 2nd Annual Conference, Stanford University, CA, Morgan Kaufmann, San Mateo, CA, 1997, pp. 423-430.
8. W. Banzhaf, P. Nordin, R.E. Keller, F.D. Francone, Genetic Programming - An Introduction on the Automatic Evolution of Computer Programs and its Applications, Morgan Kaufmann, San Mateo, CA, 1998.
9. S. Brave, The evolution of memory and mental models using genetic programming, in: J.R. Koza, D.E. Goldberg, D.B. Fogel, R.L. Riolo (Eds.), Genetic Programming 1996: Proc. 1st Annual Conference, Stanford University, CA, MIT Press, Cambridge, MA, 1996, pp. 261-266.
10. S. Brave, Using genetic programming to evolve recursive programs for tree search, in: P.J. Angeline, K.E. Kinnear Jr. (Eds.), Advances in Genetic Programming 2, MIT Press, Cambridge, MA, 1996, Chapter 10.
11. N.L. Cramer, A representation for the adaptive generation of simple sequential programs, in: J.J. Grefenstette (Ed.), Proc. International Conference on Genetic Algorithms and the Applications, Carnegie-Mellon University, Pittsburgh, PA, 1985, pp. 183-187.
12. H.B. Cribbs, R.E. Smith, Classifier systems renaissance: New analogies, new directions, in: J.R. Koza, D.E. Goldberg, D.B. Fogel, R.L. Riolo (Eds.), Genetic Programming 1996: Proc. 1st Annual Conference, Stanford University, CA, MIT Press, Cambridge, MA, 1996, pp. 547-552.
13. J. Daida, Algorithm discovery using the genetic programming paradigm: Extracting low-contrast curvilinear features from SAR images of arctic ice, in: P.J. Angeline, K.E. Kinnear Jr. (Eds.), Advances in Genetic Programming 2, MIT Press, Cambridge, MA, 1996, Chapter 21.
14. F. Dellaert, R.D. Beer, Co-evolving body and brain in autonomous agents using a developmental model, in: Technical Report CES-94-16, Department of Computer Engineering and Science, Case Western Reserve University, Cleveland, OH, 1994.
15. L. Fogel, P.J. Angeline, D. Fogel, An evolutionary programming approach to self-adaptation on finite state machines, in: J. McDonnell, R. Reynolds, D. Fogel (Eds.), Proc. 4th Annual Conference on Evolutionary Programming, MIT Press, Cambridge, MA, 1995.
16. L. Fogel, A. Owens, M. Walsh, Artificial Intelligence through Simulated Evolution, Wiley, New York, 1966.
17. J. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, 1975.
18. J.H. Holland, J.S. Reitman, Cognitive systems based on adaptive algorithms, in: Pattern Directed Inference Systems, Academic Press, New York, 1978.
19. K.E. Kinnear Jr., Generality and difficulty in genetic programming: Evolving a sort, in: S. Forrest (Ed.), Proc. 5th International Conference on Genetic Algorithms, ICGA-93, Morgan Kaufmann, San Mateo, CA, 1993, pp. 287-294.
20. J. Koza, Genetic Programming, MIT Press, Cambridge, MA, 1992.
21. J. Koza, Genetic Programming 2, MIT Press, Cambridge, MA, 1994.
22. W.B. Langdon, Evolving data structures with genetic programming, in: S. Forrest (Ed.), Proc. 6th International Conference on Genetic Algorithms, Pittsburgh, PA, Morgan Kaufmann, San Mateo, CA, 1995.
23. W.B. Langdon, Data structures and genetic programming, in: P.J. Angeline, K.E. Kinnear Jr. (Eds.), Advances in Genetic Programming 2, MIT Press, Cambridge, MA, 1996.
24. T. Nguyen, T. Huang, Evolvable 3D modeling for model-based object recognition systems, in: K.E. Kinnear Jr. (Ed.), Advances in Genetic Programming, MIT Press, Cambridge, MA, 1994.
25. E.H.N. Oakley, The application of genetic programming to the investigation of short, noisy, chaotic data series, in: T.C. Fogarty (Ed.), Lecture Notes in Computer Science: Evolutionary Computation, Springer, Berlin, 1994.
26. R. Olsson, Inductive functional programming using incremental program transformation, Ph.D. Thesis, University of Oslo, 1995.
27. R. Poli, Discovery of symbolic, neuro-symbolic and neural networks with parallel distributed genetic programming, in: Technical Report CSRP-96-14, School of Computer Science, University of Birmingham, 1996.
28. J.P. Rosca, D.H. Ballard, Discovery of subroutines in genetic programming, in: P.J. Angeline, K.E. Kinnear Jr. (Eds.), Advances in Genetic Programming 2, MIT Press, Cambridge, MA, 1996, pp. 177-202, Chapter 9.
29. D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning internal representations by error propagation, in: Parallel Distributed Processing, MIT Press, Cambridge, MA, 1986.
30. K. Sharman, A. Alcazar, Y. Li, Evolving signal processing algorithms by genetic programming, in: Proc. 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, GALESIA, 1995.
31. R.S. Sutton, Learning to predict by the methods of temporal differences, in: Proc. International Conference on Machine Learning, AAAI Press, Menlo Park, CA, 1988.
32. W.A. Tackett, Genetic programming for feature discovery and image discrimination, in: S. Forrest (Ed.), Proc. 5th International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1993.
33. A. Teller, The evolution of mental models, in: K.E. Kinnear Jr. (Ed.), Advances in Genetic Programming, MIT Press, Cambridge, MA, 1994, pp. 199-220.
34. A. Teller, Turing completeness in the language of genetic programming with indexed memory, in: Proc. 1st IEEE World Congress on Computational Intelligence, IEEE Press, 1994, pp. 136-146.
35. A. Teller, Evolving programmers: The co-evolution of intelligent recombination operators, in: K.E. Kinnear Jr., P.J. Angeline (Eds.), Advances in Genetic Programming 2, MIT Press, Cambridge, MA, 1996.
36. A. Teller, M. Veloso, Algorithm evolution for face recognition: What makes a picture difficult, in: Proc. International Conference on Evolutionary Computation, IEEE Press, 1995.
37. A. Teller, M. Veloso, A controlled experiment: Evolution for learning difficult image classification, in: Proc. 7th Portuguese Conference on Artificial Intelligence, Springer, Berlin, 1995, pp. 165-176.
38. A. Teller, M. Veloso, Program evolution for data mining, in: S. Louis (Ed.), The International Journal of Expert Systems. Third Quarter (Special Issue on Genetic Algorithms and Knowledge Bases), JAI Press, 1995, pp. 216-236.
39. A. Teller, M. Veloso, PADO: A new learning architecture for object recognition, in: K. Ikeuchi, M. Veloso (Eds.), Symbolic Visual Learning, Oxford University Press, Oxford, 1997.
40. A. Teller, Algorithm evolution with internal reinforcement for signal understanding, Ph.D. Thesis, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, 1998.
41. S. Thrun, T.M. Mitchell, Learning one more thing, Technical Report CMU-CS-94-184, Computer Science Department, Carnegie Mellon University, 1994.
42. C.J.C.H. Watkins, Learning from delayed rewards, Ph.D. Thesis, King's College, 1989.
43. S.W. Wilson, D.E. Goldberg, A critical review of classifier systems, in: Proc. 3rd International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 1989.
44. S.W. Wilson, Hierarchical credit allocation in a classifier system, in: Genetic Algorithms and Simulated Annealing, Morgan Kaufmann, San Mateo, CA, 1987.