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

This title covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode.

For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.

The memory storage requirements of complex programs are difficult to manage correctly by hand. This text shows how dynamic memory can be recycled automatically to guarantee error-free memory management. Each of the most important algorithms is explained in detail.

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 work develops in depth the idea that recurrent rules can produce rich and complicated behaviours. Distinguishing agents from their interactions, it argues that it is the computational properties of interactions that account for much of what we think of as beautiful and interesting .