Evolutionary computation: toward a new philosophy of machine intelligence
This 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.
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Computer Simulation of Natural Evolution
Theoretical and Empirical Properties of Evolutionary Computation
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Evolutionary Computation: Toward a New Philosophy of Machine Intelligence
David B. Fogel
Limited preview - 2006
adaptive Artificial Intelligence Atmar behavior best-evolved Bremermann checkers Chellapilla and Fogel chromosome 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 Genetic Programming genotype global global optimum Holland human IEEE IEEE Trans improvement indicated individual initial input iterated learning Machine Learning Mayr mechanism method Morgan Kaufmann move mutation Natural Selection 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 Theoretical tic-tac-toe tion tionary trials Turing Turing Test uniform crossover variation operators vector