An Introduction to Genetic Algorithms - Boente

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Appendix B: Other Resources Roughgarden, J., Bergman, A., Shafir, S., and Taylor, C. 1996. Adaptive computation in ecology and evolution: A guide for future research. In R. K. Belew and M. Mitchell, eds., Adaptive Individuals in Evolving Populations: Models and Algorithms. Addison−Wesley. Rumelhart, D. E., Hinton, G. E., and Williams, R. J. 1986. Learning internal representations by error propagation. In D. E. Rumelhart, J. L. McClelland, and 1986. PDP Research Group,Parallel Distributed Processing, Volume 1: Foundations. MIT Press. Rumelhart, D. E., McClelland, J. L., and 1986. PDP Research Group Parallel Distributed Processing,. Volume 1: Foundations. MIT Press. Schaefer, C. G. 1987. The ARGOT strategy: Adaptive representation genetic optimizer technique. In J. J. Grefenstette, ed., Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms. Erlbaum. Schaffer, J. D., Caruana, R. A., Eshelman, L. J, and Das, R. 1989. A study of control parameters affecting online performance of genetic algorithms for function optimization. In J. D. Schaffer, ed., Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann. Schaffer, J. D., Eshelman, L. J, and Offut, D. 1991. Spurious correlations and premature convergence in genetic algorithms. In G. Rawlins, ed., Foundations of Genetic Algorithms. Morgan Kaufmann. Schaffer, J. D., and Morishima, A. 1987. An adaptive crossover distribution mechanism for genetic algorithms. In J. J. Grefenstette, ed., Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms. Erlbaum. Schaffer, J. D., Whitley, D., and Eshelman, L. J. 1992. Combinations of genetic algorithms and neural networks: A survey of the state of the art. In L. D. Whitley and J. D. Schaffer, eds., COGANN−92: International Workshop on Combinations of Genetic Algorithms and Neural Networks. IEEE Computer Society Press. Schraudolph, N. N., and R. K. Belew 1992. Dynamic parameter encoding for genetic algorithms. Machine Learning 9: 9–21. Schulze−Kremer, S. 1992. Genetic algorithms for protein tertiary structure prediction. In R. Männer and B. Manderick, eds., Parallel Problem Solving from Nature 2. North−Holland. Schwefel, H. −P. 1975. Evolutionsstrategie und numerische Optimierung. Ph.D. thesis, Technische Universität Berlin. Schwefel, H. −P. 1977. Numerische Optimierung von Computer−Modellen mittels der Evolutionsstrategie. Basel: Birkhäuser. 156
Appendix B: Other Resources Simpson, G. G. 1953. The Baldwin effect. Evolution 7: 110–117. Smith, R. E., Forrest, S., and Perelson, A. S. 1993. Population diversity in an immune system model: Implications for genetic search. In L. D. Whitley, ed., Foundations of Genetic Algorithms 2. Morgan Kaufmann. Smolensky, P. 1988. On the proper treatment of connectionism. Behavioral and Brain Sciences 11, no. 1: 1–14. Spears, W. M. 1993. Crossover or mutation? In L. D. Whitley, ed.,Foundations of Genetic Algorithms 2. Morgan Kaufmann. Spears, W. M., and De Jong, K. A. 1991. On the virtues of parameterized uniform crossover. In R. K. Belew and L. B. Booker, eds., Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann. Syswerda, G. 1989. Uniform crossover in genetic algorithms. In J. D. Schaffer, ed., Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann. Syswerda, G. 1991. A study of reproduction in generational and steady−state genetic algorithms. In G. Rawlins, ed.,Foundations of Genetic Algorithms. Morgan Kaufmann. Tackett, W. A. 1994. Recombination, Selection, and the Genetic Construction of Computer Programs. Ph.D. thesis, University of Southern California. Department of Computer Engineering, Tanese, R. 1989. Distributed Genetic Algorithms for Function Optimization. Ph.D. thesis, University of Michigan. Electrical Engineering and Computer Science Department, Thierens, D., and Goldberg, D. E. 1993. Mixing in genetic algorithms. In S. Forrest, ed., Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann. Todd, P. M., and Miller, G. F. 1993. Parental guidance suggested: How parental imprinting evolves through sexual selection as an adaptive learning mechanism. Adaptive Behavior 2, no. 1: 5–47. Toffoli, T., and Margolus, N. 1987. Cellular Automata Machines: A New Environment for Modeling. MIT Press. Urey, H. C. 1952. The Planets: Their Origin and Development. Yale University Press. 157
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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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competition in the d *···* parti
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schemas with the best observed fitn
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Steepest−ascent hill climbing (SA
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(If the algorithm spends only 1/m o
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strings—then in principle crossov
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(4.7) Chapter 4: Theoretical Founda
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2. 3. Calculate the fitness f(x) of
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Page 152 and 153: Appendix B: Other Resources Forrest
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