An Introduction to Genetic Algorithms - Boente

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Morgan Kaufmann. Appendix B: Other Resources Das, R., Mitchell, M., and Crutchfield, J. P. 1994. A genetic algorithm discovers particle−based computation in cellular automata. In Y. Davidor, H. −P. Schwefel, and R. Männer, eds., Parallel Problem Solving from Nature—PPSN III. Springer−Verlag (Lecture Notes in Computer Science, volume 866). Das, R., and Whitley, L. D. 1991. The only challenging problems are deceptive: Global search by solving order 1 hyperplanes. In R. K. Belew, and L. B. Booker, eds., Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann. Davis, L. D. 1989. Adapting operator probabilities in genetic algorithms. In J. D. Schaffer, ed., Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann. Davis, L. D., ed. 1991. Handbook of Genetic Algorithms. Van Nostrand Reinhold. Davis, T. E., and Principe, J. C. 1991. A simulated annealing−like convergence theory for the simple genetic algorithm. In R. K. Belew and L. B. Booker, eds., Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann. Deb, K., and Goldberg, D. E. 1989. An investigation of niche and species formation in genetic function optimization. In J. D. Schaffer, ed., Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann. Deb, K., and Goldberg, D. E. 1993. Analyzing deception in trap functions. In In L. D. Whitley, ed., Foundations of Genetic Algorithms 2. Morgan Kaufmann. De Jong, K. A. 1975. An Analysis of the Behavior of a Class of Genetic Adaptive Systems. Ph.D. thesis, University of Michigan, Ann Arbor. De Jong, K. A. 1993. Genetic algorithms are NOT function optimizers. In L. D. Whitley, ed., Foundations of Genetic Algorithms 2. Morgan Kaufmann. De Jong, K. A., and Sarma, J. 1993. Generation gaps revisited. In L. D. Whitley, Foundations of Genetic Algorithms 2. Morgan Kaufmann. de la Maza, M., and Tidor, B. 1991. Boltzmann Weighted Selection Improves Performance of Genetic Algorithms. A.I. Memo 1345, MIT Artificial Intelligence Laboratory. de la Maza, M., and Tidor, B. 1993. An analysis of selection procedures with particular attention paid to proportional and Boltzmann selection. In S. Forrest, ed., Proceedings of the Fifth International Conference on 146
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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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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
Page 114 and 115: Chapter 4: Theoretical Foundations
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 144 and 145: Appendix A: Selected General Refere
Page 146 and 147: Evolution Artificielle Foundations
Page 148 and 149: Kaufmann. Appendix B: Other Resourc
Page 152 and 153: Appendix B: Other Resources Forrest
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Page 156 and 157: Kirkpatrick, S., Gelatt, C.D., Jr.,
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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An Introduction to Genetic Algorithms - Boente

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