An introduction to genetic algorithms, using research in the field to enable readers to implement and experiment with algorithms on their own. The book focuses on topics which include machine learning and artificial life and describes applications in dynamic systems, biology and ecology.

This book describes the theory, operation, and application of genetic algorithms-search algorithms based on the mechanics of natural selection and genetics.

Provides comprehensive, current, and correct information on problem solving using modern heuristics. It covers classic methods of optimisation, including dynamic programming, the simplex method, and gradient techniques, as well as recent innovations such as simulated annealing, tabu search, and evolutionary computation.

This text takes an interdisciplinary approach to the problems inherent in data mining. Drawing on contributions from information science, computer science and statistics, the book analyzes the difficulties of storing, accessing, modelling and understanding very large data sets.

An introduction to multiagent systems and contemporary distributed artificial intelligence, this text provides coverage of basic topics as well as closely-related ones. It emphasizes aspects of both theory and application and includes exercises of varying degrees of difficulty.

Shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems.