An Introduction to Genetic Algorithms - Boente

More documents

Recommendations

Info

Table of Contents Chapter 4: Theoretical Foundations of Genetic Algorithms........................................................................87 Overview................................................................................................................................................87 4.1 SCHEMAS AND THE TWO−ARMED BANDIT PROBLEM.....................................................87 The Two−Armed Bandit Problem............................................................................................88 Sketch of a Solution..................................................................................................................89 Interpretation of the Solution....................................................................................................91 Implications for GA Performance.............................................................................................92 Deceiving a Genetic Algorithm................................................................................................93 Limitations of "Static" Schema Analysis..................................................................................93 4.2 ROYAL ROADS.............................................................................................................................94 Royal Road Functions...............................................................................................................94 Experimental Results................................................................................................................95 Steepest−ascent hill climbing (SAHC).....................................................................................96 Next−ascent hill climbing (NAHC)..........................................................................................96 Random−mutation hill climbing (RMHC)...............................................................................96 Analysis of Random−Mutation Hill Climbing.........................................................................97 Hitchhiking in the Genetic Algorithm......................................................................................98 An Idealized Genetic Algorithm...............................................................................................99 4.3 EXACT MATHEMATICAL MODELS OF SIMPLE GENETIC ALGORITHMS.....................103 Formalization of GAs.............................................................................................................103 Results of the Formalization...................................................................................................108 A Finite−Population Model....................................................................................................108 4.4 STATISTICAL−MECHANICS APPROACHES.........................................................................112 THOUGHT EXERCISES....................................................................................................................114 COMPUTER EXERCISES.................................................................................................................116 5.1 WHEN SHOULD A GENETIC ALGORITHM BE USED?........................................................116 5.2 ENCODING A PROBLEM FOR A GENETIC ALGORITHM...................................................117 Binary Encodings....................................................................................................................117 Many−Character and Real−Valued Encodings......................................................................118 Tree Encodings.......................................................................................................................118 5.3 ADAPTING THE ENCODING....................................................................................................118 Inversion.................................................................................................................................119 Evolving Crossover "Hot Spots"............................................................................................120 Messy Gas...............................................................................................................................121 5.4 SELECTION METHODS.............................................................................................................124 Fitness−Proportionate Selection with "Roulette Wheel" and "Stochastic Universal" Sampling................................................................................................................................124 Sigma Scaling.........................................................................................................................125 Elitism.....................................................................................................................................126 Boltzmann Selection...............................................................................................................126 Rank Selection........................................................................................................................127 Tournament Selection.............................................................................................................127 Steady−State Selection...........................................................................................................128 5.5 GENETIC OPERATORS..............................................................................................................128 Crossover................................................................................................................................128 Mutation..................................................................................................................................129 Other Operators and Mating Strategies..................................................................................130 5.6 PARAMETERS FOR GENETIC ALGORITHMS.......................................................................130 THOUGHT EXERCISES....................................................................................................................132 COMPUTER EXERCISES.................................................................................................................133
Table of Contents Chapter 6: Conclusions and Future Directions............................................................................................135 Overview..............................................................................................................................................135 Incorporating Ecological Interactions..................................................................................................136 Incorporating New Ideas from Genetics..............................................................................................136 Incorporating Development and Learning...........................................................................................137 Adapting Encodings and Using Encodings That Permit Hierarchy and Open−Endedness.................137 Adapting Parameters............................................................................................................................137 Connections with the Mathematical Genetics Literature.....................................................................138 Extension of Statistical Mechanics Approaches..................................................................................138 Identifying and Overcoming Impediments to the Success of GAs......................................................138 Understanding the Role of Schemas in GAs........................................................................................138 Understanding the Role of Crossover..................................................................................................139 Theory of GAs With Endogenous Fitness...........................................................................................139 Appendix A: Selected General References...................................................................................................140 Appendix B: Other Resources.......................................................................................................................141 SELECTED JOURNALS PUBLISHING WORK ON GENETIC ALGORITHMS..........................141 SELECTED ANNUAL OR BIANNUAL CONFERENCES INCLUDING WORK ON GENETIC ALGORITHMS................................................................................................................141 INTERNET MAILING LISTS, WORLD WIDE WEB SITES, AND NEWS GROUPS WITH INFORMATION AND DISCUSSIONS ON GENETIC ALGORITHMS........................................142 Bibliography........................................................................................................................................143
Page 2 and 3: An Introduction to Genetic Algorith
Page 6 and 7: Chapter 1: Genetic Algorithms: An O
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 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 42 and 43: up most of the computation time. Ch
Page 46 and 47: the prospect of using GAs to automa
Page 48 and 49: where Ã is the standard deviation
Page 54 and 55:
the brain. In a feedforward network
Page 56 and 57:
Chapter 2: Genetic Algorithms in Pr
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 116 and 117:
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 126 and 127:
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 134 and 135:
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 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
show all

An Introduction to Genetic Algorithms - Boente

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

Delete template?

Save as template?