An Introduction to Genetic Algorithms - Boente

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Appendix B: Other Resources Mitchell, M., Holland, J. H., and Forrest, S. 1994. When will a genetic algorithm outperform hill climbing? In J. D. Cowan, G. Tesauro, and J. Alspector, eds., Advances in Neural Information Processing Systems 6. Morgan Kaufmann. Mitchell, M., Hraber, P. T., and Crutchfield, J. P. 1993. Revisiting the edge of chaos: Evolving cellular automata to perform computations. Complex Systems 7, 89–130. Montana, D. J., and L. D. Davis 1989. Training feedforward networks using genetic algorithms. In Proceedings of the International Joint Conference on Artificial Intelligence. Morgan Kaufmann. Mühlenbein, H. 1992. How genetic algorithms really work: 1. Mutation and hillclimbing. In R. Männer and B. Manderick, eds., Parallel Problem Solving from Nature 2. North−Holland. Nilsson, N. J. 1989. Action networks. In J. Tenenberg et al., eds.,Proceedings from the Rochester Planning Workshop: From Formal Systems to Practical Systems, Technical Report 284, University of Rochester Computer Science Department, (N.Y.). Nix, A. E., and Vose, M. D. 1991. Modeling genetic algorithms with Markov chains. Annals of Mathematics and Artificial Intelligence 5: 79–88. O'Reilly, U. −M., and Oppacher, F. 1992. An experimental perspective on genetic programming. In R. Männer and B. Manderick, eds., Parallel Problem Solving from Nature 2. NorthHolland. O'Reilly, U. −M, and Oppacher, F. 1994a. Program search with a hierarchical variable length representation: Genetic programming, simulated annealing, and hill climbing. In Y. Davidor, H. −P. Schwefel, and R. Männer, eds., Parallel Problem Solving from Nature—PPSN III. SpringerVerlag (Lecture Notes in Computer Science, Volume 866). O'Reilly, U. −M., and Oppacher, F. 1994b. Program search with a hierarchical variable length representation: Genetic programming, simulated annealing, and hill climbing. In Y. Davidor, H. −P. Schwefel, and R. Männer, eds., Parallel Problem Solving from Nature—PPSN III. SpringerVerlag (Lectures Notes in Computer Science, Volume 866). O'Reilly, U. −M., and Oppacher, F. 1995. The troubling aspects of a building block hypothesis for genetic programming. In L. D. Whitley and M. D. Vose, eds.,Foundations of Genetic Algorithms 3. Morgan Kaufmann. Osborne, H. F. 1896. A mode of evolution requiring neither natural selection nor the inheritance of acquired characteristics. Transactions of the New York Academy of Science 15: 141–142, 148. 154
Appendix B: Other Resources Otto, S. P. 1991. On evolution under sexual and viability selection: A two−locus diploid model. Evolution 45, no. 6: 1443–1457. Packard, N. H. 1988. Adaptation toward the edge of chaos. In J. A. S. Kelso, A. J. Mandell, M. F. Shlesinger, eds., Dynamic Patterns in Complex Systems. World Scientific. Packard, N. H. 1990. A genetic learning algorithm for the analysis of complex data. Complex Systems 4, no. 5: 543–572. Parsons, R., Forrest, S., and Burks, C. In press. Genetic operators for the DNA fragment assembly problem. Machine Learning. Parsons, R., Forrest, S., and Burks, C. 1993. Genetic algorithms for DNA sequence assembly. In L. Hunter, D. Searls, and J. Shavlik, eds., Proceedings of the First International Conference on Intelligent Systems for Molecular Biology, 310–318. AAAI Press. Peck, C. C., and Dhawan, A. P. 1993. A Review and Critique of Genetic Algorithm Theories. Technical Report TR 153/6/93/ECE, College of Engineering University of Cincinnati. Department of Electrical and Computer Engineering, Prügel−Bennett, A., and Shapiro, J. L. 1994. An analysis of genetic algorithms using statistical mechanics. Physical Review Letters 72, no. 9: 1305–1309. Radcliffe, N. J. 1991. Equivalence class analysis of genetic algorithms. Complex Systems 5, no. 2: 183–205. Rawlins, G., ed. 1991. Foundations of Genetic Algorithms. Morgan Kaufmann. Rechenberg, I. 1965. Cybernetic Solution Path of an Experimental Problem. Ministry of Aviation, Royal Aircraft Establishment (U.K.). Rechenberg, I. 1973. Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution. Frommann−Holzboog (Stuttgart). Reed, J., Toombs, R., and Barricelli, N. A. 1967. Simulation of biological evolution and machine learning. Journal of Theoretical Biology 17: 319–342. Richards, F. C., Meyer, T. P., and Packard, N. H. 1990. Extracting cellular automaton rules directly from experimental data. Physica D 45: 189–202. Riolo, R. L. 1992. Survival of the fittest bits. Scientific American, 267, no. 1: 114–116. 155
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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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Page 160 and 161: Appendix B: Other Resources Roughga
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