David B. Fogel – författare

Blondie24 tells the story of a computer that taught itself to play checkers far better than its creators ever could by using a program that emulated the basic principles of Darwinian evolution--random variation and natural selection-- to discover on its own how to excel at the game. Unlike Deep Blue, the celebrated chess machine that beat Garry Kasparov, the former world champion chess player, this evolutionary program didn''t have access to strategies employed by human grand masters, or to databases of moves for the endgame moves, or to other human expertise about the game of chekers. With only the most rudimentary information programmed into its "brain," Blondie24 (the program''s Internet username) created its own means of evaluating the complex, changing patterns of pieces that make up a checkers game by evolving artificial neural networks---mathematical models that loosely describe how a brain works.It''s fitting that Blondie24 should appear in 2001, the year when we remember Arthur C. Clarke''s prediction that one day we would succeed in creating a thinking machine. In this compelling narrative, David Fogel, author and co-creator of Blondie24, describes in convincing detail how evolutionary computation may help to bring us closer to Clarke''s vision of HAL. Along the way, he gives readers an inside look into the fascinating history of AI and poses provocative questions about its future.- Brings one of the most exciting areas of AI research to life by following the story of Blondie24''s development in the lab through her evolution into an expert-rated checkers player, based on her impressive success in Internet competition.- Explains the foundations of evolutionary computation, simply and clearly.- Presents complex material in an engaging style for readers with no background in computer science or artificial intelligence.- Examines foundational issues surrounding the creation of a thinking machine.- Debates whether the famous Turing Test really tests for intelligence.- Challenges deeply entrenched myths about the successes and implication of some well-known AI experiments.- Shows Blondie''s moves with checkerboard diagrams that readers can easily follow.

The definitive survey of computational intelligence from luminaries in the field Computational intelligence is a fast-moving, multidisciplinary field - the nexus of diverse technical interest areas that include neural networks, fuzzy logic, and evolutionary computation. Keeping up with computational intelligence means understanding how it relates to an ever-expanding range of applications. This is the book that ties it all together - and puts that understanding well within your reach.In Computational Intelligence: The Experts Speak, editors David B. Fogel and Charles J. Robinson present an unmatched compilation of expanded papers from plenary and special lecturers attending the 2002 IEEE World Congress on Computational Intelligence. Collectively, these papers provide a compelling snapshot of the issues that define the industry, as observed by some of the top minds in the computational intelligence community. In a series of topical chapters, this comprehensive volume shows how current technology is shaping computational intelligence, and it delivers eye-opening insights into the field's future challenges.The research detailed here covers an array of leading-edge applications, from coevolutionary robotics to underwater sensors and cognitive science, in such areas as: Self-organizing systemsSituation awarenessHuman-machine interactionAutomatic controlData recognitionComputational Intelligence also includes introductions to each grouping of contributions that provide helpful tutorials and discuss important parallels between topics. Whatever your role might be in this dynamic, influential field, this is the one reference that no practitioner of computational intelligence should be without.

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.

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.

Featuring copious introductory material by distinguished scientist Dr. David B. Fogel, this formidable collection of 30 landmark papers spans the entire history of evolutionary computation--from today's investigations back to its very origins more than 40 years ago. Chapter by chapter, Fogel highlights how early ideas have developed into current thinking and how others have been lost and await rediscovery. The introductions to each chapter reflect Fogel's one-on-one conversations with the authors and their colleagues, conducted over a period of four years. Evolutionary Computation: The Fossil Record provides in-depth historical information and technical detail that is simply unmatched in the field. This volume is complete with an extensive bibliography of related literature. Evolutionary Computation: The Fossil Record will be of particular interest to researchers and students in need of a comprehensive resource on this fascinating area of computer science. Historians will also find the book thoroughly engaging.

Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another This book covers the three fundamental topics that form the basis of computational intelligence:  neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation. Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networksCovers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integralsExamines evolutionary optimization, evolutionary learning and problem solving, and collective intelligenceIncludes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problemsFundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.

Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another 

This book covers the three fundamental topics that form the basis of computational intelligence:  neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation.

Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals Examines evolutionary optimization, evolutionary learning and problem solving, and collective intelligence Includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems

Fundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.

Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another 

This book covers the three fundamental topics that form the basis of computational intelligence:  neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation.

Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals Examines evolutionary optimization, evolutionary learning and problem solving, and collective intelligence Includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems

Fundamentals of Computational intelligence is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.

Blondie24 tells the story of a computer that taught itself to play checkers far better than its creators ever could by using a program that emulated the basic principles of Darwinian evolution--random variation and natural selection-- to discover on its own how to excel at the game. Unlike Deep Blue, the celebrated chess machine that beat Garry Kasparov, the former world champion chess player, this evolutionary program didn't have access to strategies employed by human grand masters, or to databases of moves for the endgame moves, or to other human expertise about the game of chekers. With only the most rudimentary information programmed into its "brain," Blondie24 (the program's Internet username) created its own means of evaluating the complex, changing patterns of pieces that make up a checkers game by evolving artificial neural networks---mathematical models that loosely describe how a brain works.It's fitting that Blondie24 should appear in 2001, the year when we remember Arthur C. Clarke's prediction that one day we would succeed in creating a thinking machine. In this compelling narrative, David Fogel, author and co-creator of Blondie24, describes in convincing detail how evolutionary computation may help to bring us closer to Clarke's vision of HAL. Along the way, he gives readers an inside look into the fascinating history of AI and poses provocative questions about its future.

Brings one of the most exciting areas of AI research to life by following the story of Blondie24's development in the lab through her evolution into an expert-rated checkers player, based on her impressive success in Internet competition. Explains the foundations of evolutionary computation, simply and clearly. Presents complex material in an engaging style for readers with no background in computer science or artificial intelligence. Examines foundational issues surrounding the creation of a thinking machine. Debates whether the famous Turing Test really tests for intelligence. Challenges deeply entrenched myths about the successes and implication of some well-known AI experiments. Shows Blondie's moves with checkerboard diagrams that readers can easily follow.

No pleasure lasts long unless there is variety in it. Publilius Syrus, Moral Sayings We've been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It's heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he'd seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support.

No pleasure lasts long unless there is variety in it. Publilius Syrus, Moral Sayings We've been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It's heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he'd seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support.

''I will tel! you'' the hermit said to Lancelot ''the right of the matter.'' Anonymous, The Quest of the Holy Grail Gyorgy Polya''s How to Solve It [287] stands as one of the most important contributions to the problem-solving literatme in the twentieth century. Even now, as we move into the new millennium, the book continues tobe a favorite among teachers and students for its instructive heuristics. The first edition of the book appeared in 1945, near the end of the Second World War and a few years before the invention of the transistor. The book was a quick success, and a second edition came out in 1957. How to Solve It is a compendium of approaches for tackling problems as we find them in mathematics. That is, the book provides not only examples of techniques and procedures, but also instruction on how to make analogies, use auxiliary devices, work backwards from the goal to the given, and so forth. Es­ sentially, the book is an encyclopedia of problem-solving methods to be carried out by hand, but more than that, it is a treatise on how to think about framing and attacking problems.