Evolutionary Computation: Toward a New Philosophy of Machine IntelligenceThis Third Edition provides the latest tools and techniques that enable computers to learn The Third Edition of this internationally acclaimed publication provides the latest theory and techniques for using simulated evolution to achieve machine intelligence. As a leading advocate for evolutionary computation, the author has successfully challenged the traditional notion of artificial intelligence, which essentially programs human knowledge fact by fact, but does not have the capacity to learn or adapt as evolutionary computation does. Readers gain an understanding of the history of evolutionary computation, which provides a foundation for the author's thorough presentation of the latest theories shaping current research. Balancing theory with practice, the author provides readers with the skills they need to apply evolutionary algorithms that can solve many of today's intransigent problems by adapting to new challenges and learning from experience. Several examples are provided that demonstrate how these evolutionary algorithms learn to solve problems. In particular, the author provides a detailed example of how an algorithm is used to evolve strategies for playing chess and checkers. As readers progress through the publication, they gain an increasing appreciation and understanding of the relationship between learning and intelligence. Readers familiar with the previous editions will discover much new and revised material that brings the publication thoroughly up to date with the latest research, including the latest theories and empirical properties of evolutionary computation. The Third Edition also features new knowledge-building aids. Readers will find a host of new and revised examples. New questions at the end of each chapter enable readers to test their knowledge. Intriguing assignments that prepare readers to manage challenges in industry and research have been added to the end of each chapter as well. This is a must-have reference for professionals in computer and electrical engineering; it provides them with the very latest techniques and applications in machine intelligence. With its question sets and assignments, the publication is also recommended as a graduate-level textbook. |
Contents
| 1 | |
2 Natural Evolution | 33 |
3 Computer Simulation of Natural Evolution | 59 |
4 Theoretical and Empirical Properties of Evolutionary Computation | 105 |
5 Intelligent Behavior | 183 |
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Evolutionary Computation: Toward a New Philosophy of Machine Intelligence David B. Fogel No preview available - 2006 |
Common terms and phrases
adaptive Angeline artificial intelligence Atmar Bäck best-evolved Bremermann checkers Chellapilla and Fogel chromosome coding complete Conf convergence crossover Cybernetics D. B. Fogel described distribution edited environment evaluated Evolution Strategies evolutionary algorithm Evolutionary Computation Evolutionary Programming evolved experiments Figure fitness FSMs function fuzzy Gaussian gene Genetic Algorithms genotype global global optimum Holland human IEEE IEEE Trans improvement indicated individual initial input iterated Jong learning Mayr mechanism method Morgan Kaufmann move mutation neural network nodes offered offspring opponent optimization optimum organisms output parameter parents payoff perceptron performance phenotypic play player Pleiotropy population possible prediction probability problem Proc procedure random variable randomly Rechenberg recombination reproduction rithms sampling San Mateo schemata Schraudolph Schraudolph and Belew Schwefel score sequence solution specific standard deviation strings symbols theorem tic-tac-toe tion tionary traveling salesman problem trials Turing Turing Test uniform crossover vector
Popular passages
Page 3 - What will happen when a machine takes the part of A in this game?' Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, 'Can machines think?
Page 4 - I believe that in about fifty years' time it will be possible to programme computers, with a storage capacity of about 109, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent, chance of making the right identification after five minutes of questioning. The original question, ' Can machines think ? ' I believe to be too meaningless to deserve discussion.
Page 7 - I've been able to find. It is very interesting to me to note that the computer had to make several star moves in order to get the win, and that I had several opportunities to draw otherwise. That is why I kept the game going. The machine, therefore, played a perfect ending without one misstep. In the matter of the end game, I have not had such competition from any human being since 1954, when I lost my last game.