Recent years have seen a sharp increase in the application of evolutionary computation techniques within the domain of games. Situated at the forefront of this research tidal wave, Moshe Sipper and his group have produced a plethora of award-winning results, in numerous games of diverse natures, evidencing the success and efficiency of evolutionary algorithms in general—and genetic programming in particular—at producing top-notch, human-competitive game strategies. From classic chess and checkers, through simulated car racing and virtual warfare, to mind-bending puzzles, this book serves both as a tour de force of the research landscape and as a guide to the application of evolutionary computation within the domain of games.
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By Moshe Sipper
Jan 5, 2012
*** An outstanding, timely book in the rapidly growing area of computational intelligence in games. A must read for both the neophyte and the seasoned researcher, with all the hallmarks of a landmark book. >> John Koza, author of "Genetic Programming" tetralogy *** In "Evolved to Win" Moshe Sipper provides a treasure trove of detailed examples and advice on using evolutionary computation, in conjunction with human expertise, to solve hard puzzles and to win a wide variety of challenging games. Sipper and his colleagues know this field better than anyone else, having produced some of the field's strongest and most exciting results, and this book provides a comprehensive tour of their results along with ample guidance for newcomers to the field. >> Lee Spector, Professor of Computer Science, Hampshire College, and Editor-in-Chief of the journal "Genetic Programming and Evolvable Machine" *** Written by a top player in the field of computational... More > intelligence in games, Moshe Sipper presents a cornucopia of games played---and won---by strategies attained through automatic programming. Whether you're a student or an experienced researcher, "Evolved to Win" is undoubtedly the book for you. >> Natalio Krasnogor, Technical Editor-in-Chief of the journal "Memetic Computing"< Less
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