Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science.
The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.
Classic introduction to the evolution programming techniques Many figures and tables The systematic approach makes the book an appropriate text for a senior undergraduate/graduate one semester course
Zbigniew Michalewicz
Optimierung Suchalgorithmen algorithms data structures evolutionary computation genetic algorithms genetic programming heuristics learning machine learning optimization probabilistic algorithms probabilistische Algorithmen programming search techniques