Practical Handbook of Genetic Algorithms

Chambers, Lance D.

• ContentsIntroductionMulti-Niche Crowding for Multi-modal SearchIntroduction 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 VehicleOverview Introduction to Artificial Neural Networks Introduction to ALVINN The Evolutionary Approach Task Specifics Implementation and Results Conclusions Future Directions Locating Putative Protein Signal SequencesIntroduction Implementation Results of Sample Applications Parametrization Study Future Directions Selection Methods for Evolutionary AlgorithmsFitness 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 PlanningIntroduction Principles of Genetic Algorithms The Search Algorithm The Explore Algorithm The Ariadne’s CLEW Algorithm Parallel Implementation Conclusion, Results, and Perspective The Boltzmann Selection ProcedureIntroduction Empirical Analysis Introduction to Boltzmann Selection Theoretical Analysis Discussion and Related Work Conclusion Structure and Performance of Fine-Grain Parallelism in Genetic SearchIntroduction Three Fine