Beschreibung
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 Mühlenbein, 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 Computation is 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.
Inhaltsverzeichnis
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. 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: Open Problems 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. 4.5. Research Areas Supportive of Human-Competitive Results. 6. Promising Application Areas for Genetic and Evolutionary Computation. 7. Acknowledgements. 10: Case Based Reasoning; V. Balaraman. 1. Introduction. 2. Case-Based Reasoning. 3. Case Memory as an Evolutionary System. 4. Hybrid Systems. 5. Evolving Higher Levels. 6. Conclusions. 11: The Challenge Of Complexity; W. Banzhaf, J. Miller. 1. GP Basics and State of the Art. 2. The Situation in Biology. 3. Nature''s way to deal with complexity. 4. What we can learn from Nature? 5. A possible scenario: Transfer into Genetic Programming. 6. Conclusion. Author Index.
