An Introduction to Genetic Algorithms - Boente

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Chapter 2: Genetic Algorithms in Problem Solving Exactly how is the GA solving the problem? What are the schemas that are being processed? What is the role of crossover in finding a good solution? Uniform crossover of the type used here has very different properties than single−point crossover, and its use makes it harder to figure out what schemas are being recombined. Meyer (personal communication) found that turning crossover off and relying solely on the four mutation operators did not make a big difference in the GA's performance; as in the case of genetic programming, this raises the question of whether the GA is the best method for this task. An interesting extension of this work would be to perform control experiments comparing the performance of the GA with that of other search methods such as hill climbing. To what extent are the results restricted by the fact that only certain conditions are allowed (i.e., conditions that are conjunctions of ranges on independent variables)? Packard (1990) proposed a more general form for conditions that also allows disjunctions (('s); an example might be where we are given two nonoverlapping choices for the conditions on x6. A further generalization proposed by Packard would be to allow disjunctions between sets of conditions. To what extent will this method succeed on other types of prediction tasks? Packard (1990) proposes applying this method to tasks such as weather prediction, financial market prediction, speech recognition, and visual pattern recognition. Interestingly, in 1991 Packard left the Physics Department at the University of Illinois to help form a company to predict financial markets (Prediction Company, in Santa Fe, New Mexico). As I Figure 2.13: Results of the four highest−fitness condition sets found by the GA. (See figure 2.12.) Each plot shows trajectories of data points that satisfied that condition set. The leftmost white region is the initial 50 time steps during which data were taken. The vertical lines in that region represent the various conditions on given in the condition set. The vertical line on the right−hand side represents the time at which the prediction is to be made. Note how the trajectories narrow at that region, indicating that the GA has found conditions for good predictability. (Reprinted from Martin Casdagli and Stephen Eubank (eds.), Nonlinear Modeling and Forecasting; © 1992 Addison−Wesley Publishing Company, Inc. Reprinted by permission of the publisher.) write this (mid 1995), the company has not yet gone public with their results, but stay tuned. 46
Predicting Protein Structure Chapter 2: Genetic Algorithms in Problem Solving One of the most promising and rapidly growing areas of GA application is data analysis and prediction in molecular biology. GAs have been used for, among other things, interpreting nuclear magnetic resonance data to determine the structure of DNA (Lucasius and Kateman 1989), finding the correct ordering for an unordered group of DNA fragments (Parsons, Forrest, and Burks, in press), and predicting protein structure. Here I will describe one particular project in which a GA was used to predict the structure of a protein. Proteins are the fundamental functional building blocks of all biological cells. The main purpose of DNA in a cell is to encode instructions for building up proteins out of amino acids; the proteins in turn carry out most of the structural and metabolic functions of the cell. A protein is made up of a sequence of amino acids connected by peptide bonds. The length of the sequence varies from protein to protein but is typically on the order of 100 amino acids. Owing to electrostatic and other physical forces, the sequence "folds up" to a particular three−dimensional structure. It is this three−dimensional structure that primarily determines the protein's function. The three−dimensional structure of a Crambin protein (a plant−seed protein consisting of 46 amino acids) is illustrated in figure 2.14. The three−dimensional structure of a protein is determined by the particular sequence of its amino acids, but it is not currently known precisely how a given sequence leads to a given structure. In fact, being able to predict a protein's structure from its amino acid sequence is one of the most important unsolved problems of molecular biology and biophysics. Not only would a successful prediction algorithm be a tremendous advance in the understanding of the biochemical mechanisms of proteins, but, since such an algorithm could conceivably be used to design proteins to carry out specific functions, it would have profound, far−reaching effects on biotechnology and the treatment of disease. Recently there has been considerable effort toward developing methods such as GAs and neural networks for automatically predicting protein structures (see, for example, Hunter, Searls, and Shavlik 1993). The relatively simple GA prediction project of Steffen Schulze−Kremer (1992) illustrates one way in which GAs can be used on this task; it also illustrates some potential pitfalls. Schulze−Kremer took the amino acid sequence of the Crambin protein and used a GA to search in the space of possible structures for one that would fit well with Crambin's amino acid sequence. The most straight−forward way to describe the structure of a protein is to list the three−dimensional coordinates of each amino acid, or even each atom. In principle, a GA could use such a representation, evolving vectors of coordinates to find one that resulted in a plausible structure. But, because of a number of difficulties with that representation (e.g., the usual crossover and mutation operators would be too likely to create physically impossible structures), Schulze−Kremer instead described protein structures using "torsion angles"—roughly, the angles made by the peptide bonds connecting amino acids and the angles made by bonds in an amino acid's "side chain." (See Dickerson and Geis 1969 for an overview of how three−dimensional protein structure is measured.) Schulze−Kremer used 10 torsion angles to describe each of the N (46 in the case of Crambin) amino acids in the sequence for a given protein. This collection ofN sets of 10 torsion angles completely defines the three−dimensional structure of the protein. A chromosome, representing a candidate structure with N amino acids, thus contains N sets of ten real numbers. This representation is illustrated in figure 2.15. 47
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 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 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
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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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2. 3. Calculate the fitness f(x) of
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Defining ri,j(k) and is somewhat tr
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Results of the Formalization How ca
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Chapter 4: Theoretical Foundations
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Figure 4.5: Predicted and observed
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COMPUTER EXERCISES 1. 2. 3. 4. 5. 6
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prone to rather arbitrary orderings
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Inversion works by choosing two poi
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The one hitch is that, as was seen
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used in the work of Tanese (1989),
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Two individuals are chosen at rando
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strategies community, in which para
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* 10. * 11. * 12. * Chapter 4: Theo
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Chapter 6: Conclusions and Future D
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Chapter 6: Conclusions and Future D
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Appendix A: Selected General Refere
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Evolution Artificielle Foundations
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Kaufmann. Appendix B: Other Resourc
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Morgan Kaufmann. Appendix B: Other
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Appendix B: Other Resources Forrest
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Appendix B: Other Resources Grefens
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Kirkpatrick, S., Gelatt, C.D., Jr.,
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Appendix B: Other Resources Mitchel
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Appendix B: Other Resources Roughga
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Appendix B: Other Resources Vose, M
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An Introduction to Genetic Algorithms - Boente

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