An Introduction to Genetic Algorithms - Boente

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Appendix B: Other Resources Forrest, S., and Mitchell, M. 1993a. What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation. Machine Learning 13: 285–319. Forrest, S., and Mitchell, M. 1993b. Relative building block fitness and the building block hypothesis. In L. D. Whitley, ed., Foundations of Genetic Algorithms 2. Morgan Kaufmann. Fraser, A. S. 1957a. Simulation of genetic systems by automatic digital computers: I. Introduction. Australian Journal of Biological Science 10: 484–491. Fraser, A. S. 1957b. Simulation of genetic systems by automatic digital computers: II. Effects of linkage on rates of advance under selection. Australian Journal of Biological Science 10: 492–499. French, R. M., and Messinger, A. 1994. Genes, phenes, and the Baldwin effect: Learning and evolution in a simulated population. In R. A. Brooks P. Maes, eds., Artificial Life IV. MIT Press. Friedman, G. J. 1959. Digital simulation of an evolutionary process. General Systems Yearbook 4: 171–184. Fujiki, C., and Dickinson, J. 1987. Using the genetic algorithm to generate Lisp source code to solve the Prisoner's dilemma. In J. J. Grefenstette, ed., Proceedings of the First International Conference on Genetic Algorithms and Their Application. Erlbaum. Gacs, P., Kurdyumov, G. L., and Levin, L. A. 1978. One−dimensional uniform arrays that wash out finite islands. Problemy Peredachi Informatsii 14: 92–98 (in Russian). Glover, F. 1989. Tabu search. Part I. ORSA Journal of Computing 1: 190–206. Glover, F. 1990. Tabu search. Part II. ORSA Journal of Computing 2: 4–32. Goldberg, D. E. 1987. Simple genetic algorithms and the minimal deceptive problem. In L. D. Davis, ed.,Genetic Algorithms and Simulated Annealing. Morgan Kaufmann. Goldberg, D. E. 1989a. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison−Wesley. Goldberg, D. E. 1989b. Genetic algorithms and Walsh functions: Part I, A gentle introduction. Complex Systems 3: 129–152. Goldberg, D. E. 1989c. Genetic algorithms and Walsh functions: Part II, Deception and its analysis. Complex Systems 3: 153–171. 148
Appendix B: Other Resources Goldberg, D. E. 1989d. Sizing populations for serial and parallel genetic algorithms. In J. D. Schaffer, ed., Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann. Goldberg, D. E. 1990. A note on Boltzmann tournament selection for genetic algorithms and population−oriented simulated annealing. Complex Systems 4: 445–460. Goldberg, D. E., and Deb, K. 1991. A comparitive analysis of selection schemes used in genetic algorithms. In G. Rawlins, Foundations of Genetic Algorithms. Morgan Kaufmann. Goldberg, D. E., Deb, K., andKorb, B. 1990. Messy genetic algorithms revisited: Studies in mixed size and scale. Complex Systems 4: 415–444. Goldberg, D. E., Deb, K., Kargupta, H., and Harik, G. 1993. Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. In S. Forrest, ed., Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann. Goldberg, D. E., Korb, B., and Deb, K. 1989. Messy genetic algorithms: Motivation, analysis, and first results., Complex Systems 3: 493–530. Goldberg, D. E., and Richardson, J. 1987. Genetic algorithms with sharing for multimodal function optimization. In J. J. Grefenstette, ed., Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms. Erlbaum. Goldberg, D. E., and Segrest, P. 1987. Finite Markov chain analysis of genetic algorithms. In J. J. Grefenstette, ed., Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms. Erlbaum. Gonzaga de Sa, P., and Maes, C. 1992. The Gacs−Kurdyumov−Levin automaton revisited. Journal of Statistical Physics 67, no. 3/4: 507–522. Grefenstette, J. J. 1986. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics 16, no. 1: 122–128. Grefenstette, J. J. 1991a. Lamarckian learning in multi−agent environments. In R. K. Belew, and L. B. Booker, eds., Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann. Grefenstette, J. J. 1991b. Conditions for implicit parallelism. In G. Rawlins, ed., Foundations of Genetic Algorithms. Morgan Kaufmann. 149
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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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Page 160 and 161: Appendix B: Other Resources Roughga
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