An Introduction to Genetic Algorithms - Boente

More documents

Recommendations

Info

Chapter 2: Genetic Algorithms in Problem Solving turn activate the output units via the same procedure. Use Montana and Davis's method to evolve weights wi, j (0 d wi, j d 1) and thresholds Ãj (0dÃjd1) to solve this problem. Put the wi,j values on the same chromosome. (The dj values are ignored by the input nodes, which are always set to 0 or 1.) The fitness of a chromosome is the average sum of the squares of the errors (differences between the output and input patterns at each position) over the entire training set. How well does the GA succeed? For the very ambitious reader: Compare the performance of the GA with that of back−propagation (Rumelhart, Hinton, and Williams 1986a) in the same way that Montana and Davis did. (This exercise is intended for those already familiar with neural networks.) 64
Chapter 3: Genetic Algorithms in Scientific Models Overview Genetic algorithms have been for the most part techniques applied by computer scientists and engineers to solve practical problems. However, John Holland's original work on the subject was meant not only to develop adaptive computer systems for problem solving but also to shed light, via computer models, on the mechanisms of natural evolution. The idea of using computer models to study evolution is still relatively new and is not widely accepted in the evolutionary biology community. Traditionally, biologists have used several approaches to understanding evolution, including the following: Examining the fossil record to determine how evolution has proceeded over geological time. Examining existing biological systems in their natural habitats in order to understand the evolutionary forces at work in the process of adaptation. This includes both understanding the role of genetic mechanisms (such as geographical effects on mating) and understanding the function of various physical and behavioral characteristics of organisms so as to infer the selective forces responsible for the evolution of these adaptations. Performing laboratory experiments in which evolution over many generations in a population of relatively simple organisms is studied and controlled. Many such experiments involve fruit flies (Drosophila) because their life span and their reproductive cycle are short enough that experimenters can observe natural selection over many generations in a reasonable amount of time. Studying evolution at the molecular level by looking at how DNA and RNA change over time under particular genetic mechanisms, or by determining how different evolutionarily related species compare at the level of DNA so as to reconstruct "phylogenies" (evolutionary family histories of related species). Developing mathematical models of evolution in the form of equations (representing properties of genotypes and phenotypes and their evolution) that can be solved (analytically or numerically) or approximated. These are the types of methods that have produced the bulk of our current understanding of natural evolution. However, such methods have a number of inherent limitations. The observed fossil record is almost certainly incomplete, and what is there is often hard to interpret; in many cases what is surmised from fossils is intelligent guesswork. It is hard, if not impossible, to do controlled experiments on biological systems in nature, and evolutionary time scales are most often far too long for scientists to directly observe how biological systems change. Evolution in systems such as Drosophila can be observed to a limited extent, but many of the important questions in evolution (How does speciation take place? How did multicellular organisms come into being? Why did sex evolve?) cannot be answered by merely studying evolution in Drosophila. The molecular level is often ambiguous—for example, it is not clear what it is that individual pieces of DNA encode, or how they work together to produce phenotypic traits, or even which pieces do the encoding and which are "junk DNA" (noncoding regions of the chromosome). Finally, to be solvable, mathematical models of evolution must be simplified greatly, and it is not obvious that the simple models provide insight into real evolution. The invention of computers has permitted a new approach to studying evolution and other natural systems: simulation. A computer program can simulate the evolution of populations of organisms over millions of 65
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:
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 20 and 21: Chapter 1: Genetic Algorithms: An O
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 58 and 59: Figure 2.21: An illustration of Mil
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 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 82 and 83: an initial population in which the
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: 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 126 and 127:
Chapter 4: Theoretical Foundations
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:
Chapter 4: Theoretical Foundations
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?