"This is a useful introduction to the subject and is well worth reading as an entry into evolutionary computing." -- Chris Robbins,Computing

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research,including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics.

Synopsis
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This introduction describes research in the field and also enables readers to implement and experiment with genetic algorithms on their own. The book focuses in depth on a small set of important topics - particularly in machine learning, scientific modelling and artificial life - and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modelling projects stretch beyond the strict boundaries of computer science to include dynamic systems theory, game theory, molecular biology, ecology, evolutionary biology and population genetics.