Choice and development

ABSTRACT

The process of development creates a phenotype from one or more genotypes of an individual through interaction with an environment. The opportunity for development to choose a phenotype from a set of alternatives made possible by the individual's genotype(s) has not been widely considered in evolutionary computation. We briefly review recent research on developmental learning, dominance, and hybrid genetic algorithms that has investigated the role of choice in development. A new model of probabilistic development is presented based upon genotypes that encode the probabilities that the various alleles are expressed in the phenotype. The model outperforms a standard, binary haploid model on two families of single-peaked fitness functions in terms of average fitness. The standard model performed better on multi-peaked MAXSAT environments. More research is needed to fully evaluate the new model.

REFERENCES

• 1: Ancel, L. W., "Undermining the Baldwin expediting effect: Does phenotypic plasticity accelerate evolution?", Theoretical Population Biology, 58, 307--319, 2000.
• 2: Baldwin, J. M., "A new factor in evolution", American Naturalist, 30, 441--451, 1896.
• 3: Richard K. Belew , Melanie Mitchell, Adaptive individuals in evolving populations: models and algorithms, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1996
• 4: Eric Bonabeau , Marco Dorigo , Guy Theraulaz, Swarm intelligence: from natural to artificial systems, Oxford University Press, Inc., New York, NY, 1999
• 5: Thang Nguyen Bui , Byung Ro Moon, Genetic Algorithm and Graph Partitioning, IEEE Transactions on Computers, v.45 n.7, p.841-855, July 1996 [doi>10.1109/12.508322]
• 6: Farley, A. M., "Developmental learning in single-peaked environments", in submission, 2007.
• 7: Farley, A. M., "Domination in development", in submission, 2007.
• 8: David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989
• 9: Gruau, F., Whitley, D., and Pyeatt, L., "A comparison between cellular encoding and direct encoding for genetic neural networks", Proc. Genetic Programming Conf., (1996), 81--89.
• 10: Hacker, K. and Lewis, K., "Robust design through use of a hybrid genetic algorithm", Proceedings of DETC'02 ASME, 2002, 1--10.
• 11: Don Coppersmith , David Gamarnik , Mohammad Hajiaghayi , Gregory B. Sorkin, Random MAX SAT, random MAX CUT, and their phase transitions, Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms, January 12-14, 2003, Baltimore, Maryland
• 12: Hedrick P. W., Genetics of Populations, Jones and Bartlett, Sudbury, MA, (2000).
• 13: Hinton, G. E and Nowlan, S. J., "How learning can guide evolution", Complex Systems, 1, p. 495--502, 1987.
• 14: Prasanna Jog , Jung Y. Suh , Dirk Van Gucht, The Effects of Population SizeHeuristic Crossover and Local Improvement on a Genetic Algorithm for the Traveling Salesman Problem, Proceedings of the 3rd International Conference on Genetic Algorithms, p.110-115, June 01, 1989
• 15: Kauffman, S. A., The Origins of Order, New York: Oxford University Press, (1993).
• 16: Kinnebrock, W., "Accelerating the standard back propagation method using a genetic approach", Neurocomputing, 6, (1994), 583--588.
• 17: John R. Koza, Genetic Programming IV: Routine Human-Competitive Machine Intelligence, Kluwer Academic Publishers, Norwell, MA, 2003
• 18: Mayley, G. "Guiding or hiding: Explorations into the effects of learning on the rate of evolution", In Proceedings of the Fourth European Conference on Artificial Life 97, 135--144. 1997.
• 19: Melanie Mitchell, An introduction to genetic algorithms, MIT Press, Cambridge, MA, 1996
• 20: Maynard Smith, J., The Theory of Evolution, 3rd Edition, New York: Cambridge Univ. Press, (1993).
• 21: Maynard Smith, J., Evolutionary Genetics, 2nd Edition, New York: Oxford University Press, (1998).
• 22: Mendel, G., "Experiments in plant hybridization", 1866, reprinted in Stern, C. and Sherwood, E. R. (eds), The Origins of Genetics. A Mendel Source Book. San Francisco: Freeman, 1966, see also http://www.mendelweb.org/Mendel.html.
• 23: Il-Seok Oh , Jin-Seon Lee , Byung-Ro Moon, Hybrid Genetic Algorithms for Feature Selection, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.26 n.11, p.1424-1437, November 2004 [doi>10.1109/TPAMI.2004.105]
• 24: Sarkar, M. and Yegnanarayana,B., "Feedforward neural networks configuration using evolutionary programming", Proceeding IEEE Intl. Conf. on Neural Networks, (1997), 438--443.
• 25: Siegal, M. L., and Bergman, A. "Waddington's canalization revisited: Developmental stability and evolution", Proc. Nat. Acad. Sci, 99(16), (2002) 10528--10532.
• 26: Simpson, G. G., "The Baldwin Effect", Evolution, 7, 1953, 110--117, reprinted in {5}.
• 27: Vonk, E., Jain, L., and Johnson, R., "Using genetic algorithms with grammar encoding to generate neural networks", Proceeding IEEE Intl. Conf. on Neural Networks, (1995), 1928--1931.
• 28: Waddington, C. H., "Canalization of development and the inheritance of acquired characters", Nature, 3811, 1942, 563--565, reprinted in BM.
• 29: Weber, B. H. and Depew, D. J., Evolution and learning: The Baldwin effect reconsidered, Cambridge, MA : MIT Press, 2003.
• 30: Wright, S. "Evolution in Mendelian populations", Genetics, 16, (1931), 1423--1447, (1999).
• 31: Yao, X.. "Evolving artificial neural networks", Proceedings of the IEEE, 87(9), (1931), 97--159.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search

General Terms:
Algorithms

Keywords:
choice, development, dominance, genetic algorithms, learning