An Introduction to Genetic Algorithms - Boente

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Evolution Artificielle Foundations of Genetic Algorithms Genetic Programming Conference IEEE Conference on Evolutionary Computation International Conference on Genetic Algorithms International Conference on Artificial Neural Networks and Genetic Algorithms International Joint Conference on Artificial Intelligence Golden West International Conference on Intelligent Systems Machine Learning Neural Information Processing Systems Parallel Problem Solving from Nature Simulation of Adaptive Behavior World Congress on Neural Networks INTERNET MAILING LISTS, WORLD WIDE WEB SITES, AND NEWS GROUPS WITH INFORMATION AND DISCUSSIONS ON GENETIC ALGORITHMS ga−list (mailing list on general GA topics) (to subscribe, send an email request to .) genetic−programming (mailing list on genetic programming) (to subscribe, send an email request to .) gann (mailing list on combining GAs and neural networks) (to subscribe, send a request to .) GA−List WWW site: http://www.aic.nrl.navy.mil/galist (This page has many pointers to other pages related to GAs, as well as GA source code.) ALife Online WWW site: http://alife.santafe.edu (This page has many pointers to information on GAs and artificial life.) comp.ai.genetic (USENET news group) Appendix B: Other Resources 142
comp.ai.alife (USENET news group) ENCORE (Evolutionary Computation Repository Network—a collection of information on evolutionary computation):ftp://alife.santafe.edu/pub/USER−AREA/EC/ Bibliography Appendix B: Other Resources Ackley, D., and Littman, M. 1992. Interactions between learning and evolution. In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, eds., Artificial Life II. Addison−Wesley. Ackley, D., and Littman, M. 1994. A case for Lamarckian evolution. In C. G. Langton, ed., Artificial Life III, Addison−Wesley. Altenberg, L. 1994. The evolution of evolvability in genetic programming. In K. E. Kinnear, Jr., ed., Advances in Genetic Programming. MIT Press. Altenberg, L. 1995. The Schema Theorem and Price's Theorem. In L. D. Whitley and M. D. Vose, eds, Foundations of Genetic Algorithms 3. Morgan Kaufmann. Angeline, P. J., and Pollack, J. B. 1992. The evolutionary induction of subroutines. In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society. Erlbaum. Antonisse, J. 1989. A new interpretation of schema notation that overturns the binary encoding constraint. In J. D. Schaffer, ed., Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann. Axelrod, R. 1984. The Evolution of Cooperation. Basic Books. Axelrod, R. 1987. The evolution of strategies in the iterated Prisoner's Dilemma. In L. D. Davis, ed., Genetic Algorithms and Simulated Annealing. Morgan Kaufmann Axelrod, R., and Dion,D. 1988. The further evolution of cooperation. Science 242, no. 4884: 1385–1390. Bäck, T., and Hoffmeister, F. 1991. Extended selection mechanisms in genetic algorithms. In R. K. Belew and L. B. Booker, eds., Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann. Bäck, T., Hoffmeister, F., and Schwefel, H. −P. 1991. A survey of evolution strategies. In R. K. Belew and L. B. Booker, eds., Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan 143
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An Introduction to Genetic Algorith
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Table of Contents Chapter 4: Theore
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Chapter 1: Genetic Algorithms: An O
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1.4 SEARCH SPACES AND FITNESS LANDS
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potential energy is a measure of ho
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A simple method of implementing fit
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from an initial state to a goal. Fo
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Robert Axelrod of the University of
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instances of H at time t, and let
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What is the total payoff after 10 g
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6. * c. d. a. b. Chapter 1: Genetic
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As a simple example, consider a pro
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Chapter 2: Genetic Algorithms in Pr
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it to get one fitness case correct:
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Evolving Cellular Automata Chapter
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up most of the computation time. Ch
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the prospect of using GAs to automa
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where Ã is the standard deviation
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the brain. In a feedforward network
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Figure 2.21: An illustration of Mil
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experiments with grammatical encodi
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function of the average error of th
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COMPUTER EXERCISES 1. 2. 3. 4. 5. 6
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simulated generations, and such sim
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environments, they can fairly quick
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Chapter 3: Genetic Algorithms in Sc
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Figure 3.6: A schematic illustratio
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an initial population in which the
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these random effects, Bedau and Pac
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3. * 4. * the female child in the n
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calculating the observed average fi
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(4.1) The goal is to find n = n* th
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Page 100 and 101: Steepest−ascent hill climbing (SA
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Page 108 and 109: 2. 3. Calculate the fitness f(x) of
Page 110 and 111: Defining ri,j(k) and is somewhat tr
Page 112 and 113: Results of the Formalization How ca
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Page 118 and 119: Figure 4.5: Predicted and observed
Page 120 and 121: COMPUTER EXERCISES 1. 2. 3. 4. 5. 6
Page 122 and 123: prone to rather arbitrary orderings
Page 124 and 125: Inversion works by choosing two poi
Page 128 and 129: The one hitch is that, as was seen
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Page 138 and 139: * 10. * 11. * 12. * Chapter 4: Theo
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Page 152 and 153: Appendix B: Other Resources Forrest
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Page 158 and 159: Appendix B: Other Resources Mitchel
Page 160 and 161: Appendix B: Other Resources Roughga
Page 162: Appendix B: Other Resources Vose, M
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