Genetic Algorithms and Evolutionary Computation

List of Figures. List of Tables. Preface. Contributing Authors. 1: Towards a Theory of Organisms and Evolving Automata; H. Muhlenbein. 1. Introduction. 2. Evolutionary computation and theories of evolution. 3. Darwin's continental cycle conjecture. 4. The system view of evolution. 5. Von Neumann's self-reproducing automata. 6. Turing's intelligent machine. 7. What can be computed by an artificial neural network? 8. Limits of computing and common sense. 9. A logical theory of adaptive systems. 10. The lambda-Calculus for creating artificial intelligence. 11. Probabilistic logic. 12. Stochastic analysis of cellular automata. 13. Stochastic analysis of evolutionary algorithms. 14. Stochastic analysis and symbolic representations. 15. Conclusion. 2: Two Grand Challenges for EC; K. De Jong. 1. Introduction. 2. Historical Diversity. 3. The Challenge of Unfication. 4. The Challenge of Expansion. 5. Summary and Conclusions. 3: Evolutionary Computation: Challenges and duties; C. Cotta, P. Moscato. 1. Introduction. 2. Challenge #1: Hard problems for the paradigm - Epistasis and Parameterized Complexity. 3. Challenge #2: Systematic design of provably good recombination operators. 4. Challenge #3: Using Modal Logic and Logic Programming methods to guide the search. 5. Challenge #4: Learning from other metaheuristics and other open challenges. 6. Conclusions. 4: OpenProblems in the Spectral Analysis of Evolutionary Dynamics; L. Altenberg. 1. Optimal Evolutionary Dynamics for Optimization. 2. Spectra for Finite Population Dynamics. 3. Karlin's Spectral Theorem for Genetic Operator Intensity. 4. Conclusion. 5: Solving Combinatorial Optimization Problems via Reformulation and Adaptive Memory Meta- heuristics; G.A. Kochenberger, F. Glover, B. Alidaee, C. Rego. 1. Introduction. 2. Transformations. 3. Examples. 4. Solution Approaches. 5. Computational Experience. 6. Summary. 6: Problems in Optimization; W.G. Macready. 1. Introduction. 2. Foundations. 3. Connections. 4. Applications. 5. Conclusions. 7: EC Theory - 'In Theory'; C.R. Stephens, R. Poli. 8: Asymptotic Convergence of Scaled Genetic Algorithms; L.M. Schmitt. 1. Notation and Preliminaries. 2. The Genetic Operators. 3. Convergence of Scaled Genetic Algorithms to Global Optima. 4. Future Extensions of the Theory. 5. Appendix: Proof of some basic or technical results. 9: The Challenge of Producing Human-Competitive Results by Means of Genetic and Evolutionary Computation; J.R. Koza, M.J. Streeter, M.A. Keane. 1. Turing's Prediction Concerning Genetic and Evolutionary Computation. 2. Definition of Human-Competitiveness. 3. Desirable Attributes of the Pursuit of Human-Competitiveness. 4.<