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-organism
Contents Introduction Multi-Niche Crowding for Multi-modal Search Introduction Genetic Algorithms for Multi-modal Search Application of MNC to Multi-modal Test Functions Application to DNA Restriction Fragment Map Assembly Results and Discussion Conclusions Previous Related Work and Scope of Present Work Appendix Artificial Neural Network Evolution: Learning to Steer a Land Vehicle Overview Introduction to Artificial Neural Networks Introduction to ALVINN The Evolutionary Approach Task Specifics Implementation and Results Conclusions Future Directions Locating Putative Protein Signal Sequences Introduction Implementation Results of Sample Applications Parametrization Study Future Directions Selection Methods for Evolutionary Algorithms Fitness Proportionate Selection (FPS) Windowing Sigma Scaling Linear Scaling Sampling Algorithms Ranking Linear Ranking Exponential Ranking Tournament Selection Genitor or Steady State Models Evolution Strategy and Evolutionary Programming Methods Evolution Strategy Approaches Top-n Selection Evolutionary Programming Methods The Effects of Noise Conclusions References Parallel Cooperating Genetic Algorithms: An Application to Robot Motion Planning Introduction Principles of Genetic Algorithms The Search Algorithm The Explore Algorithm The Ariadne’s CLEW Algorithm Parallel Implementation Conclusion, Results, and Perspective The Boltzmann Selection Procedure Introduction Empirical Analysis Introduction to Boltzmann Selection Theoretical Analysis Discussion and Related Work Conclusion Structure and Performance of Fine-Grain Parallelism in Genetic Search Introduction Three Fine
