Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation
New textbook on the three main aspects of computational intelligence: neural networks, fuzzy logic, and evolutionary computation. The book is unique as it was written by experts in the three respective areas, each being a former editor-in-chief of the corresponding main IEEE journal. The book is part of the IEEE Press Series on Computational Intelligence, edited by David Fogel.
Blondie24: Playing at the Edge of AI (The Morgan Kaufmann Series in Artificial Intelligence)
This is the popular science treatment of David Fogel and Kumar Chellapilla’s research into having an evolutionary algorithm optimize a neural network (called Blonde24) to play checkers. The approach used a pioneering deep learning adversarial neural network framework and was the precursor to the Blondie25 chess program that became the first machine learning chess program to defeat a human nationally ranked master as well as Fritz8, which was one of the top-5 computer chess programs in the world at the time.
How To Solve It: Modern Heuristics (2nd edition)
This popular textbook has been translated into Chinese, Greek, and Polish. As noted on Amazon.com “I used this book in a Master’s class on Heuristics (Systems Engineering, University of Virginia) and received the most positive textbook reviews I have seen in my fifteen years of teaching. The book is an excellent choice for a course on heuristics, mathematical modeling, optimization, etc., and could be used in an advanced undergraduate class or a graduate class.” The book provides insights into how to think about problem solving, not merely a listing of algorithms to implement.
Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (3rd edition)
This is the best-selling book that presents evolutionary algorithms in the context of machine intelligence, offering that intelligence is a property of a system to adapt its behavior to meet goals in a range of environments. The book is offered as a graduate text, with chapter problems, and it covers the practice and theory of evolutionary algorithms, along with their philosophical place in making truly intelligent machines.
Evolutionary Computation: The Fossil Record
This is the seminal text on the history of evolutionary algorithms from the 1950s to the 1990s. Selected papers are republished in their original form with an editorial introduction by David Fogel. The book provides the definitive history of the development of evolutionary algorithms for gaming, sequence prediction, optimization, robotics, artificial life, neural and fuzzy computation, and other innovations. This is a crucial source for graduate students who need to perform a literature review prior to embarking on doctoral research.