The second volume of two examining genetic algorithms (GA) as a means to assess or develop systems by applying the rules of reproduction, gene crossover, and mutation. Fourteen essays written by distinguished researchers examine the potentiality of GA in artificial neural network evolution, parameter estimation, the Boltzmann selection procedure, hybrid approaches for real-time sequencing and scheduling problems, and chemical engineering. The discussions use tables and graphs to help illustrate key elements. Annotation c. by Book News, Inc., Portland, Or.
The mathematics employed by genetic algorithms (GAs)are among the most exciting discoveries of the last few decades. But what exactly is a genetic algorithm? A genetic algorithm is a problem-solving method that uses genetics as its model of problem solving. It applies the rules of reproduction, gene crossover, and mutation to pseudo-organisms so those "organisms" can pass beneficial and survival-enhancing traits to new generations. GAs are useful in the selection of parameters to optimize a system's performance. A second potential use lies in testing and fitting quantitative models. Unlike any other book available, this interesting new text/reference takes you from the construction of a simple GA to advanced implementations. As you come to understand GAs and their processes, you will begin to understand the power of the genetic-based problem-solving paradigms that lie behind them.
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