Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introduces mutation, crossover, design issues of selection and replacement methods, the issue of populations size, and the question of design of the fitness function. It also includes a methodological material on efficient implementation. Some of the other topics in this book include the design of simple evolutionary algorithms, applications to several types of optimization, evolutionary robotics, simple evolutionary neural computation, and several types of automatic programming including genetic programming. The book gives applications to biology and bioinformatics and introduces a number of tools that can be used in biological modeling, including evolutionary game theory. Advanced techniques such as cellular encoding, grammar based encoding, and graph based evolutionary algorithms are also covered.

"Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets.Lots of applications and test problems, including a biotechnology chapter."

An Overview of Evolutionary Computation.- Designing Simple Evolutionary Algorithms.- Optimizing Real-Valued Functions.- Sunburn: Coevolving Strings.- Small Neural Nets : Symbots.- Evolving Finite State Automata.- Ordered Structures.- Plus-One-Recall-Store.- Fitting to Data.- Tartarus: Discrete Robotics.- Evolving Logic Functions.- ISAc List: Alternative Genetic Programming.- Graph-Based Evolutionary Algorithms.- Cellular Encoding.- Application to Bioinformatics.