An Introduction to Genetic Algorithms - Boente

More documents

Recommendations

Info

Kirkpatrick, S., Gelatt, C.D., Jr., and Vecchi, M. P. 1983. Optimization by simulated annealing. Science 220: 671–680. Kitano, H. 1990. Designing neural networks using genetic algorithms with graph generation system. Complex Systems 4: 461–476. Kitano, H. 1994. Neurogenetic learning: An integrated method of designing and training neural networks using genetic algorithms. Physica D 75: 225–238. Knuth, D. E. 1973. The Art of Computer Programming. Volume 3: Searching and Sorting. Addison−Wesley. Koza, J. R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press. Koza, J. R. 1994. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press. Langton, C. G. 1992. Introduction. In C. G. Langton, C. Taylor, J. D. Farmer, andS. Rasmussen, eds., Artificial Life II. Addison−Wesley Lenat, D. B., and Brown, J. S. 1984. Why AM and Eurisko appear to work. Artificial Intelligence 23: 260–294. Levenick, J. R. 1991. Inserting introns improves genetic algorithm success rate: Taking a cue from biology. In R. K. Belew and L. B. Booker, eds., Proceedings of the Fourth International Conference on Genetic Algorithms. Morgan Kaufmann. Liepins, G. E., and Vose, M. D. 1990. Representational issues in genetic optimization. Journal of Experimental and Theoretical Artificial Intelligence 2: 101–115. Liepins, G. E., and Vose, M. D. 1991. Deceptiveness and genetic algorithm dynamics. G. Rawlins, Foundations of Genetic Algorithms. Morgan Kaufmann. Lindgren, K. 1992. Evolutionary phenomena in simple dynamics. In C. G. Langton, C. Taylor, J. D. Farmer, and S. Rasmussen, eds., Artificial Life II. Addison−Wesley. Lloyd Morgan, C. 1896. On modification and variation. Science 4: 733–740. Lucasius, C. B., and Kateman, G. 1989. In J. D. Schaffer, ed., Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann. Mackey, M. C., and Glass, L. 1977. Science 197: 297. Appendix B: Other Resources 152
Appendix B: Other Resources Martin, F. G., and Cockerham, C. C. 1960. High speed selection studies. In O. Kempthorne, ed., Biometrical Genetics. Pergamon. Mason, A. J. 1993. Crossover Non−Linearity Ratios and the Genetic Algorithm: Escaping the Blinkers of Schema Processing and Intrinsic Parallelism. Report No. 535b, University of Auckland School of Engineering, (New Zealand). Maynard Smith, J. 1987. When learning guides evolution. Nature 329: 761–762. McClelland, J. L., Rumelhart, D. E., and 1986. the PDP Research Group Parallel Distributed Processing,Volume 2: Psychological and Biological Models. MIT Press. Meyer, T. P. 1992. Long−Range Predictability of High−Dimensional Chaotic Dynamics. Ph.D. thesis, University of Illinois at Urbana−Champaign, Urbana. Meyer, T. P., and Packard, N. H. 1992. Local forecasting of high−dimensional chaotic dynamics. In M. Casdagli and S. Eubank, eds.,Nonlinear Modeling and Forecasting. Addison−Wesley. Miller, G. F. 1994. Exploiting mate choice in evolutionary computation: Sexual selection as a process of search, optimization, and diversification. In T. C. Fogarty, ed.,Evolutionary Computing: AISB Workshop, Leeds, U.K., April 1994, Selected Papers. Springer−Verlag. Miller, G. F., and Todd, P. M. 1993. Evolutionary wanderlust: Sexual selection with directional mate preferences. In J. −A. Meyer, H. L. Roitblat, and S. W. Wilson, eds., From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior. MIT Press. Miller, G. F., Todd, P. M., andHegde, S. U. 1989. Designing neural networks using genetic algorithms. In J. D. Schaffer, ed.,Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann. Mitchell, M., Crutchfield, J. P., andHraber, P. T. 1994a. Evolving cellular automata to perform computations: Mechanisms and impediments. Physica D 75: 361–391. Mitchell, M., Crutchfield, J. P., and Hraber, P. T. 1994b. Dynamics, computation, and the "edge of chaos": A re−examination. In G. Cowan, D. Pines, and D. Melzner, eds., Complexity: Metaphors, Models, and Reality. Addison−Wesley. Mitchell, M., Forrest, S., and Holland, J. H. 1992. The royal road for genetic algorithms: Fitness landscapes and GA performance. In F. J. Varela and P. Bourgine, eds., Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life. MIT Press. 153
Page 2 and 3:
An Introduction to Genetic Algorith
Page 4 and 5:
Table of Contents Chapter 4: Theore
Page 6 and 7:
Chapter 1: Genetic Algorithms: An O
Page 8 and 9:
Page 10 and 11:
1.4 SEARCH SPACES AND FITNESS LANDS
Page 12 and 13:
potential energy is a measure of ho
Page 14 and 15:
A simple method of implementing fit
Page 16 and 17:
from an initial state to a goal. Fo
Page 18 and 19:
Robert Axelrod of the University of
Page 20 and 21:
Page 22 and 23:
Page 24 and 25:
Page 26 and 27:
instances of H at time t, and let
Page 28 and 29:
What is the total payoff after 10 g
Page 30 and 31:
6. * c. d. a. b. Chapter 1: Genetic
Page 32 and 33:
As a simple example, consider a pro
Page 34 and 35:
Chapter 2: Genetic Algorithms in Pr
Page 36 and 37:
it to get one fitness case correct:
Page 38 and 39:
Evolving Cellular Automata Chapter
Page 40 and 41:
Page 42 and 43:
up most of the computation time. Ch
Page 44 and 45:
Page 46 and 47:
the prospect of using GAs to automa
Page 48 and 49:
where Ã is the standard deviation
Page 50 and 51:
Page 52 and 53:
Page 54 and 55:
the brain. In a feedforward network
Page 56 and 57:
Page 58 and 59:
Figure 2.21: An illustration of Mil
Page 60 and 61:
Page 62 and 63:
experiments with grammatical encodi
Page 64 and 65:
function of the average error of th
Page 66 and 67:
COMPUTER EXERCISES 1. 2. 3. 4. 5. 6
Page 68 and 69:
Page 70 and 71:
simulated generations, and such sim
Page 72 and 73:
environments, they can fairly quick
Page 74 and 75:
Chapter 3: Genetic Algorithms in Sc
Page 76 and 77:
Figure 3.6: A schematic illustratio
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
an initial population in which the
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
these random effects, Bedau and Pac
Page 90 and 91:
3. * 4. * the female child in the n
Page 92 and 93:
calculating the observed average fi
Page 94 and 95:
(4.1) The goal is to find n = n* th
Page 96 and 97:
competition in the d *···* parti
Page 98 and 99:
schemas with the best observed fitn
Page 100 and 101:
Steepest−ascent hill climbing (SA
Page 102 and 103:
(If the algorithm spends only 1/m o
Page 104 and 105:
strings—then in principle crossov
Page 106 and 107: (4.7) Chapter 4: Theoretical Founda
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
Page 130 and 131: used in the work of Tanese (1989),
Page 132 and 133: Two individuals are chosen at rando
Page 136 and 137: strategies community, in which para
Page 138 and 139: * 10. * 11. * 12. * Chapter 4: Theo
Page 140 and 141: Chapter 6: Conclusions and Future D
Page 142 and 143: Chapter 6: Conclusions and Future D
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 150 and 151: Morgan Kaufmann. Appendix B: Other
Page 152 and 153: Appendix B: Other Resources Forrest
Page 154 and 155: Appendix B: Other Resources Grefens
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
show all

An Introduction to Genetic Algorithms - Boente

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?