An Introduction to Genetic Algorithms - Boente

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Chapter 6: Conclusions and Future Directions suited. I believe that theoretical advances will also filter back to the evolutionary biology community. Though it hasn't happened yet, I think there is a very good chance that proving things about these simple models will lead to new ways to think mathematically about natural evolution. Evolutionary computation is far from being an established science with a body of knowledge that has been collected for centuries. It has been around for little more than 30 years, and only in the last decade have a reasonably large number of people been working on it. Almost all the projects discussed in this book still can be considered "work in progress." The projects described here were chosen because I find the work, or at least its general direction, worth pursuing. In each of the case studies I have tried to point out open questions and to give some ideas about what should be done next. My strong hope is that readers of this book will become excited or inspired enough to take some of this research further, or even to take genetic algorithms in new directions. Here is a brief list of some of the directions I think are the most important and promising. Incorporating Ecological Interactions In most GA applications the candidate solutions in the population are assigned fitnesses independent of one another and interact only by competing for selection slots via their fitnesses. However, some of the more interesting and successful applications have used more complicated "ecological" interactions among population members. Hillis's host−parasite coevolution was a prime example; so was Axelrod's experiment in which the evolving strategies for the Prisoner's Dilemma played against one another and developed a cooperative symbiosis. These methods (along with other examples in the GA literature) are not understood very well; much more work is needed, for example, on making host−parasite coevolution a more generally applicable method and understanding how it works. In addition, other types of ecological interactions, such as individual competition for resources or symbiotic cooperation in collective problem solving, can be utilized in GAs. Incorporating New Ideas from Genetics Haploid crossover and mutation are only the barest bones of real−world genetic systems. I have discussed some extensions, including diploidy, inversion, gene doubling, and deletion. Other GA researchers have looked at genetics−inspired mechanisms such as dominance, translocation, sexual differentiation (Goldberg 1989a, chapter 5), and introns (Levenick 1991). These all are likely to have important roles in nature, and mechanisms inspired by them could potentially be put to excellent use in problem solving with GAs. As yet, the exploration of such mechanisms has only barely scratched the surface of their potential. Perhaps even more potentially significant is genetic regulation. In recent years a huge amount has been learned in the genetics community about how genes regulate one another—how they turn one another on and off in complicated ways so that only the appropriate genes get expressed in a given situation. It is these regulatory networks that make the genome a complex but extremely adaptive system. Capturing this kind of genetic adaptivity will be increasingly important as GAs are used in more complicated, changing environments. 136
Incorporating Development and Learning Whereas in typical GA applications evolution works directly on a population of candidate solutions, in nature there is a separation between genotypes (encodings) and phenotypes (candidate solutions). There are very good reasons for such a separation. One is that, as organisms become more complex, it seems to be more efficient and tractable for the operators of evolution to work on a simpler encoding that develops into the complex organism. Another is that environments are often too unpredictable for appropriate behavior to be directly encoded into a genotype that does not change during an individual's life. In nature, the processes of development and learning help "tune" the behavioral parameters defined by the individual's genome so that the individual's behavior will become adapted for its particular environment. These reasons for separating genotype from phenotype and for incorporating development and learning have been seen in several of our case studies. Kitano pointed out that a grammatical encoding followed by a development phase allows for the evolution of more complex neural networks than is possible using a direct encoding. Incorporating development in this way has been taken further by Gruau (1992) and Belew (1993), but much more work needs to be done if we are to use GAs to evolve large, complex sytems (such as computational "brains"). The same can be said for incorporating learning into evolutionary computation—we have seen how this can have many advantages, even if what is learned is not directly transmitted to offspring—but the simulations we have seen are only early steps in understanding how to best take advantage of interactions between evolution and learning. Adapting Encodings and Using Encodings That Permit Hierarchy and Open−Endedness Evolution in nature not only changes the fitnesses of organisms, it also has mechanisms for changing the genetic encoding. I have discussed some reordering operators that occur in nature (e.g., inversion and translocation). In addition, genotypes have increased in size over evolutionary time. In chapter 5 I gave many reasons why the ability to adapt their own encodings is important for GAs. Several methods have been explored in the GA literature. In my opinion, if we want GAs eventually to be able to evolve complex structures, the most important factors will be open−endedness (the ability for evolution to increase the size and complexity of individuals to an arbitrary degree), encapsulation (the ability to protect a useful part of an encoding from genetic disruption and to make sure it acts as a single whole), and hierarchical regulation (the ability to have different parts of the genome regulate other parts, and likewise the ability to have different parts of the phenotype regulate other parts). Some explorations of open−endedness and encapsulation in genetic programming were discussed; to me these types of explorations seem to be on the right track, though the specific type of encoding used in GP may not turn out to be the most effective one for evolving complex structures. Adapting Parameters Chapter 6: Conclusions and Future Directions Natural evolution adapts its own parameters. Crossover and mutation rates are encoded (presumably in some rather complicated way) in the genomes of organisms, along with places where these operators are more likely to be applied (e.g., crossover hot spots). Likewise, population sizes in nature are not constant but are controlled by complicated ecological interactions. As was described in chapter 5, we would like to find similar ways of adapting the parameters for GAs as part of the evolutionary process. There have been several explorations of this already, but much more work needs to be done to develop more general and sophisticated 137
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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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