An Introduction to Genetic Algorithms - Boente

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Appendix B: Other Resources Vose, M. D. 1991. Generalizing the notion of schema in genetic algorithms. Artificial Intelligence 50: 385–396. Vose, M. D. 1993. Modeling simple genetic algorithms. In L. D. Whitley, ed., Foundations of Genetic Algorithms 2. Morgan Kaufmann. Vose, M. D., and Liepins, G. E. 1991. Punctuated equilibria in genetic search. Complex Systems 5: 31–44. Waddington, C. H. 1942. Canalization of development and the inheritance of acquired characters. Nature 150: 563–565. Walsh, J. L. 1923. A closed set of orthogonal functions. American Journal of Mathematics55: 5–24. Weber, B. H., Depew, D. J., and Smith, J. D, eds.1988. Entropy, Information, and Evolution: New Perspectives on Physical and Biological Evolution. MIT Press. Whitley, L. D. 1989. The Genitor algorithm and selection pressure: Why rank−based allocation of reproductive trials is best. In J. D. Schaffer, ed., Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann. Whitley, L. D. 1991. Fundamental principles of deception in genetic search. In G. Rawlins, ed., Foundations of Genetic Algorithms Morgan Kaufmann. Whitley, L. D. 1993a. An executable model of a simple genetic algorithm. In L. D. Whitley, ed., Foundations of Genetic Algorithms 2. Morgan Kaufmann. Whitley, L. D., ed.1993b. Foundations of Genetic Algorithms 2.Morgan Kaufmann. Whitley, L. D., and Schaffer, J. D., eds.,1992. COGANN−92: International Workshop on Combinations of Genetic Algorithms and Neural Networks. IEEE Computer Society Press. Whitley, L. D., and Vose, M. D., 1995. Foundations of Genetic Algorithms 3. Morgan Kaufmann. Winston, P. H. 1992. Artificial Intelligence, third edition. Addison−Wesley. Wolfram, S. 1986. Theory and Applications of Cellular Automata. World Scientific. Wright, A. H. 1991. Genetic algorithms for real parameter optimization. In G. Rawlins, ed., Foundations of Genetic Algorithms. Morgan Kaufmann. Wright, S. 1931. Evolution in Mendelian populations. Genetics 16: 97–159. 158
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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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Defining ri,j(k) and is somewhat tr
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Page 156 and 157: Kirkpatrick, S., Gelatt, C.D., Jr.,
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An Introduction to Genetic Algorithms - Boente

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