01498    a2200241   450000500170000000800400001702000220005703700370007904000070011604100080012307200140013107200140014507200120015907200120017107200210018307200210020410000220022524500710024725000060031826000240032430000100034852008980035820250526161932.0250430041995xx        4           eng    a9780849325298qBB  bTaylor & FranciscGBP 160.00fBB  a01  aeng7 aUY2thema7 aUB2thema7 aUY2bic7 aUB2bic7 aMAT0000002bisac7 aMAT0210002bisac1 aLance D. Chambers10aPractical Handbook of Genetic AlgorithmsbNew Frontiers, Volume II  a1  bCRC Pressc19950815  a448 p  bThe 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.