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Intelligence through Simulated Evolution
Forty Years of Evolutionary Programming
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A unique, one-stop reference to the history, technology, and application of evolutionary programmingEvolutionary programming has come a long way since Lawrence Fogel first proposed in 1961 that intelligence could be modeled on the natural process of evolution. Efforts to apply this innovative approach to artificial intelligence have also evolved over the years, and the advent of fast desktop computers capable of solving complex computational problems has spawned an explosion of interest in the field.Offering the unique perspective of one of the inventors of evolutionary programming, this remar...
A unique, one-stop reference to the history, technology, and application of evolutionary programming
Evolutionary programming has come a long way since Lawrence Fogel first proposed in 1961 that intelligence could be modeled on the natural process of evolution. Efforts to apply this innovative approach to artificial intelligence have also evolved over the years, and the advent of fast desktop computers capable of solving complex computational problems has spawned an explosion of interest in the field.
Offering the unique perspective of one of the inventors of evolutionary programming, this remarkable work traces forty years of developments in the field. Dr. Fogel consolidates a wealth of information and hard-to-find figures from across the literature, providing comprehensive coverage of the evolutionary programming approach to simulated evolution. This includes both an updated, condensed version of his bestselling 1966 work, Artificial Intelligence Through Simulated Evolution (with Owens and Walsh), and a thorough discussion of the history, technology, and methods of machine learning from 1970 to the present.
This important resource features clear, up-to-date explanations of how the simulation of evolutionary processes allows machines to learn to solve new problems in new ways. And it helps readers make the leap to generating intelligent systems-extending the discussion to neural networks, fuzzy logic, and genetic algorithms development. Engineers and computer scientists in all areas of machine learning will gain invaluable insight into existing and emerging applications and obtain ample ideas to draw upon in future research.Evolutionäre Programme suchen nach Lösungen komplizierter Probleme mit Methoden, die von der Entwicklung der Natur abgeschaut sind: Ausgehend von einer "erratenen" Anfangslösung läßt man so lange "Mutationen" zu, bis diese nur noch zu schlechteren Lösungen führen. Der Autor, einer der Erfinder der evolutionären Programmierung, zeichnet den Weg dieser Methoden vom Beginn vor etwa 40 Jahren bis heute nach. (08/99)
Evolutionary programming has come a long way since Lawrence Fogel first proposed in 1961 that intelligence could be modeled on the natural process of evolution. Efforts to apply this innovative approach to artificial intelligence have also evolved over the years, and the advent of fast desktop computers capable of solving complex computational problems has spawned an explosion of interest in the field.
Offering the unique perspective of one of the inventors of evolutionary programming, this remarkable work traces forty years of developments in the field. Dr. Fogel consolidates a wealth of information and hard-to-find figures from across the literature, providing comprehensive coverage of the evolutionary programming approach to simulated evolution. This includes both an updated, condensed version of his bestselling 1966 work, Artificial Intelligence Through Simulated Evolution (with Owens and Walsh), and a thorough discussion of the history, technology, and methods of machine learning from 1970 to the present.
This important resource features clear, up-to-date explanations of how the simulation of evolutionary processes allows machines to learn to solve new problems in new ways. And it helps readers make the leap to generating intelligent systems-extending the discussion to neural networks, fuzzy logic, and genetic algorithms development. Engineers and computer scientists in all areas of machine learning will gain invaluable insight into existing and emerging applications and obtain ample ideas to draw upon in future research.Evolutionäre Programme suchen nach Lösungen komplizierter Probleme mit Methoden, die von der Entwicklung der Natur abgeschaut sind: Ausgehend von einer "erratenen" Anfangslösung läßt man so lange "Mutationen" zu, bis diese nur noch zu schlechteren Lösungen führen. Der Autor, einer der Erfinder der evolutionären Programmierung, zeichnet den Weg dieser Methoden vom Beginn vor etwa 40 Jahren bis heute nach. (08/99)