Frontiers of Evolutionary Computation / Edition 1by Anil Menon
Pub. Date: 10/26/2007
Publisher: Springer US
Frontiers of Evolutionary Computation brings together eleven contributions by international leading researchers discussing what significant issues still remain unresolved in the field of Evolutionary Computation (EC). They explore such topics as the role of building blocks, the balancing of exploration with exploitation, the modeling of EC algorithms, the
Frontiers of Evolutionary Computation brings together eleven contributions by international leading researchers discussing what significant issues still remain unresolved in the field of Evolutionary Computation (EC). They explore such topics as the role of building blocks, the balancing of exploration with exploitation, the modeling of EC algorithms, the connection with optimization theory and the role of EC as a meta-heuristic method, to name a few. The articles feature a mixture of informal discussion interspersed with formal statements, thus providing the reader an opportunity to observe a wide range of EC problems from the investigative perspective of world-renowned researchers. These prominent researchers include: Heinz M hlenbein, Kenneth De Jong, Carlos Cotta and Pablo Moscato, Lee Altenberg, Gary A. Kochenberger, Fred Glover, Bahram Alidaee and Cesar Rego, William G. Macready, Christopher R. Stephens and Riccardo Poli, Lothar M. Schmitt, John R. Koza, Matthew J. Street and Martin A. Keane, Vivek Balaraman, Wolfgang Banzhaf and Julian Miller. Frontiers of Evolutionary Computationis ideal for researchers and students who want to follow the process of EC problem-solving and for those who want to consider what frontiers still await their exploration.
- Springer US
- Publication date:
- Genetic Algorithms and Evolutionary Computation Series, #11
- Edition description:
- Product dimensions:
- 6.10(w) x 9.25(h) x 0.03(d)
Table of Contents
List of Figures. List of Tables. Preface. Contributing Authors. 1: Towards a Theory of Organisms and Evolving Automata; H. Mühlenbein. 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. Shastic analysis of cellular automata. 13. Shastic analysis of evolutionary algorithms. 14. Shastic 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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