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Frontiers of Evolutionary Computation
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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.<

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From the reviews: "This is a diverse collection of eleven papers that share the common theme of posing open problems and pointing out possible directions of future research, all written by leading researchers in the field of evolutionary computation (EC). ... A good starting point for readers who are not familiar with evolutionary computation is provided ... . A different approach taken by several authors is the combination of EC with different fields with the aim of enriching and developing EC theory." (Thomas Jansen, Mathematical Reviews, 2005f)

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