The Practical Handbook of Genetic Algorithms: New Frontiers, Volume II (Hardcover)

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-Grain Parallel GA Topologies Performance of fgpGAs and cgpGAs Future Directions Parameter Estimation for a Generalized Parallel Loop Scheduling Algorithm Introduction Current Scheduling Algorithms A New Scheduling Methodology Results Conclusion Controlling a Dynamic Physical System Using Genetic-based Learning Methods Introduction The Control Task Previous Learning Algorithms for the Pole-Cart Problem Genetic Algorithms (GA) Generating Control Rules Using a Simple GA Implementation Details Experimental Results Difficulties with GAPOLE Approach A Different Genetic Approach for the Problem The Structured Genetic Algorithm Evolving Neuro-controllers Using sGA Fitness Measure and Reward Scheme Simulation Results Discussion A Hybrid Approach Using Neural Networks, Simulation, Genetic Algorithms, and Machine Learning for Real-time Sequencing and Scheduling Problems Introduction Hierarchical Generic Controller Implementing the Optimization Function An Example Remarks Chemical Engineering Introduction Case Study 1: Best Controller Synthesis Using Qualitative Criteria Case Study 2: Optimization of Back Mix Reactors in Series Case Study 3: Solution of Lattice Model to Predict Adsorption of Polymer Molecules Comparison with Other Techniques Vehicle Routing with Time Windows Using Genetic Algorithms Introduction Mathematical Formulation for the VRPTW The GIDEON System Computational Results Summary and Conclusions Evolutionary Algorithms and Dialogue Introduction Methodology Evolutionary Algorithms Natural Language Processing Dialogue in LOLITA Tuning the Parameters Target Dialogues Application of EAs to LOLITA Results Improving the Fitness Function Discussion Summary References Incorporating Redundancy and Gene Activation Mechanisms in Genetic Search for Adapting to Non-Stationary Environments Introduction The Structured GA Use of sGA in a Time-varying Problem Experimental Details Conclusions Input Space Segmentation with a Genetic Algorithm for Generation of Rule-based Classifier Systems Introduction A Heuristic Method Genetic Algorithm Based Method Results Appendix I: An Indexed Bibliography of Genetic Algorithms Appendix II: Publications Contract