书目详细信息
： EVOLVE - a bridge between probability, set oriented numerics and evolutionary computation /
书目信息 机读格式(MARC)
| ISBN： | 9783642327254 (alk. paper) |
|---|---|
| ISBN： | 3642327257 (alk. paper) |
| 编目源： | AU AU OCLCO VGM YDXCP OHX BTCTA BWX CHVBK OCLCF OCLCQ DLC |
| 会议名称： | EVOLVE 2011 |
| 题名： | EVOLVE - a bridge between probability, set oriented numerics and evolutionary computation / Emilia Tantar ... [and 6 others] (eds.). |
| 索书号： | TP301.6-532/E93E |
| 载体形态： | xxii, 414 pages : illustrations (some color) ; 25 cm. |
| 一般附注： | Papers presented at the international workshop EVOLVE 2011. |
| 书目附注： | Includes bibliographical references. |
| 格式化内容附注： | Machine generated contents note: pt. I Foundations, Probability and Evolutionary Computation -- 1. On the Foundations and the Applications of Evolutionary Computing / Emilia Tantar -- 1.1. Introduction -- 1.1.1. From Evolutionary Computing to Particle Algorithms -- 1.1.2. Outline of the Chapter -- 1.2. Basic Notation and Motivation -- 1.3. Genetic Particle Models -- 1.4. Positive Matrices and Particle Recipes -- 1.4.1. Positive Matrices and Measures -- 1.4.2. Interacting Particle Models -- 1.4.3. Genealogical and Ancestral Structures -- 1.4.4.Complete Genealogical Tree Model -- 1.4.5. Particle Derivation and Conditioning Principles -- 1.5. Some Application Domains -- 1.5.1. Particle Absorption Models -- 1.5.2. Signal Processing and Bayesian Inference -- 1.5.3. Interacting Kalman Filters -- 1.5.4. Stochastic Optimization Algorithms -- 1.5.5. Analysis of Convergence under Uncertain Behavior -- 1.5.6. Rare Events Stochastic Models -- References. |
| 格式化内容附注： | Contents note continued: 2. Incorporating Regular Vines in Estimation of Distribution Algorithms / Enrique R. Villa-Diharce -- 2.1. Introduction -- 2.2. Estimation of Distribution Algorithms -- 2.3. Copula Functions -- 2.3.1. The Gaussian Copula -- 2.4. Regular Vines -- 2.4.1. Copula Entropy and Mutual Information -- 2.5. EDAs Based on Regular Vines -- 2.5.1. Description of the C-Vine EDA -- 2.5.2. Description of the D-Vine EDA -- 2.5.3. Incorporating the Gaussian Copula -- 2.6. Conclusions -- References -- 3. The Gaussian Polytree EDA with Copula Functions and Mutations / Enrique Villa Diharce -- 3.1. Introduction -- 3.2. Related Work -- 3.3. The Gaussian Poly-Tree -- 3.3.1. Construction of the GPT -- 3.3.2. Simulating Data from a Poly-Tree -- 3.4. The Gaussian Poly-Tree with Gaussian Copula Function -- 3.4.1. Gaussian Copula Functions -- 3.4.2. Building the Gaussian Copula Poly-Tree and Data Simulation -- 3.5. Gaussian Poly-Trees with Gaussian Copula Functions + Mutations -- 3.6. Experiments. |
| 格式化内容附注： | Contents note continued: 3.6.1. Experiment I: Contrasting the Gaussian Poly-Tree with the Dependence Tree -- 3.6.2. Experiment 2: Solving Unimodal Functions with the GPT-EDA -- 3.6.3. Experiment 3: Solving Multimodal Functions with the GPT-EDA -- 3.6.4. Experiment 4: Solving Unimodal Functions with the GCPT-EDA -- 3.6.5. Experiment 5: Solving Multimodal Functions with the GCPT-EDA -- 3.6.6. Experiment 6: Solving Unimodal Functions with the GCPT-EDA + Mutations -- 3.6.7. Experiment 7: Solving Multimodal Functions with the GCPT-EDA + Mutations -- 3.7. Conclusions -- References -- Appendix A Test Function Difintions -- pt. II Set Oriented Numerics -- 4. On Quality Indicators for Black-Box Level Set Approximation / Johannes W. Kruisselbrink -- 4.1. Introduction -- 4.2. Related Work -- 4.3. Decision Theoretic Motivation of Quality Indicators -- 4.3.1. Pareto Order for Representativeness -- 4.3.2. Lorenz Order for Representativeness -- 4.3.3. Unary Indicators for Representativeness. |
| 格式化内容附注： | Contents note continued: 4.3.4.A Preference Order for Feasibility -- 4.3.5.Combining Representativeness and Feasibility -- 4.3.6. Diversity versus Representativeness -- 4.4. Selected Quality Indicators and Their Properties -- 4.4.1. Simple Spread Indicators -- 4.4.2. Diversity Indicators -- 4.4.3. Indicators Based on Distances between Sets -- 4.5. Numerical Results -- 4.5.1. Experimental Study -- 4.6. Summary and Outlook -- References -- 5. Set Oriented Methods for the Numerical Treatment of Multiobjective Optimization Problems / Michael Dellnitz -- 5.1. Introduction -- 5.2. Multiobjective Optimization -- 5.3.A Subdivision Algorithm for the Computation of Relative Global Attractors -- 5.3.1. The Relative Global Attractor -- 5.3.2. The Algorithm -- 5.3.3. Realization of the Algorithm -- 5.4. Basic Algorithms for Multiobjective Optimization -- 5.4.1. Subdivision Techniques -- 5.4.2. Recover Techniques in Parameter Space -- 5.4.3. Image-Set Oriented Recover Techniques. |
| 格式化内容附注： | Contents note continued: 5.5. Multiobjective Optimal Control Problems -- 5.5.1. Differentially Flat Systems -- 5.5.2. Lagrangian Systems -- 5.6. Concluding Remarks -- References -- pt. III Landscape, Coevolution and Cooperation -- 6.A Complex-Networks View of Hard Combinatorial Search Spaces / Fabio Daolio -- 6.1. Hard Problems, Search Spaces, and Fitness Landscapes -- 6.1.1. Fitness Landscapes -- 6.1.2. Local Optima Networks -- 6.1.3. Some Definitions for Weighted Complex Networks -- 6.2. Local Optima Networks of NK Landscapes -- 6.2.1. Basins of Attraction -- 6.3. LONs for the QAP Fitness Landscapes -- 6.3.1. General Network Features -- 6.3.2. Optima Distribution and Clustering -- 6.4. Conclusions and Prospects -- References -- 7. Cooperative Coevolution for Agrifood Process Modeling / Nathalie Perrot -- 7.1. Introduction -- 7.2. Modeling Agri-Food Industrial Processes -- 7.2.1. The Camembert-Cheese Ripening Process -- 7.2.2. Modeling Expertise on Cheese Ripening. |
| 格式化内容附注： | Contents note continued: 7.3. Phase Estimation Using GP -- 7.3.1. Phase Estimation Using a Classical GP -- 7.3.2. Phase Estimation Using a Parisian GP -- 7.4. Bayesian Network Structure Learning Using CCEAs -- 7.4.1. Recall of Some Probability Notions -- 7.4.2. Bayesian Networks -- 7.4.3. Evolution of an Independence Model -- 7.4.4. Sharing -- 7.4.5. Immortal Archive and Embossing Points -- 7.4.6. Description of the Main Parameters -- 7.4.7. Bayesian Network Structure Estimation -- 7.4.8. Experiments and Results -- 7.4.9. Analysis -- 7.5. Conclusion -- References -- 8. Hybridizing cGAs with PSO-like Mutation / A. Villagra -- 8.1. Introduction -- 8.2. Basic Concepts -- 8.2.1. Particle Swarm Optimization -- 8.2.2. Cellular Genetic Algorithm -- 8.3. Active Components of PSO into cGA -- 8.4. Experiments and Analysis of Results -- 8.5. Conclusions and Further Work -- References -- pt. IV Multi-objective Optimization, Heuristic Conversion Algorithms. |
| 格式化内容附注： | Contents note continued: 9. On Gradient-Based Local Search to Hybridize Multi-objective Evolutionary Algorithms / Carlos A. Coello Coello -- 9.1. Introduction -- 9.2. Descent Cones and Directions -- 9.3. Practical Approaches -- 9.3.1. Movements toward the Optimum -- 9.3.2. Movements along the Pareto Set -- 9.3.3. Directed Movements -- 9.3.4. Step-Length Computation -- 9.4. Toward the Hybridization -- 9.4.1. Main Issues -- 9.4.2. Early Hybrids -- 9.5. Conclusions and New Trends -- References -- 10. On the Integration of Theoretical Single-Objective Scheduling Results for Multi-objective Problems / Joachim Lepping -- 10.1. Introduction -- 10.2. Scheduling Problems and Theoretical Results -- 10.2.1. Single-Objective -- 10.2.2. Multi-objective -- 10.2.3. The Gap between Single-Objective Theory and Multi-objective Approaches -- 10.3. The Modular Predator-Prey Model -- 10.4. Adopting the Predator-Prey Model to Scheduling Problems -- 10.4.1. Variation Operator Design -- 10.4.2. Evaluation. |
| 格式化内容附注： | Contents note continued: 10.5. Integrating a Self-adaptive Mechanism for Diversity Preservation -- 10.5.1. Algorithmic Extension and Implementation -- 10.5.2. Evaluation -- 10.6. Conclusion -- References -- 11. Analysing the Robustness of Multiobjectivisation Approaches Applied to Large Scale Optimisation Problems / Coromoto Leon -- 11.1. Introduction -- 11.2. Optimisation Schemes -- 11.3. Multiobjectivisation -- 11.4. Parameter Setting -- 11.5. Increasing the Robustness of Multiobjectivisation -- 11.6. Experimental Evaluation -- 11.6.1. Performance of Multiobjectivisation -- 11.6.2. On the Usage of Multiobjectivisation with Parameters -- 11.6.3. Rising the Robusiness of Multiobjectivisation -- 11.6.4. Analysing the Performance of Hyperheuristics with a Large Number of Variables -- 11.7. Conclusions -- References -- 12.A Comparative Study of Heuristic Conversion Algorithms, Genetic Programming and Return Predictability on the German Market / Sebastian Jansen -- 12.1. Introduction. |
| 格式化内容附注： | Contents note continued: 12.2. Related Work -- 12.3. Problem Formulation -- 12.3.1. Moving Average Crossover (MA) -- 12.3.2. Trading Range Breakout (TRB) -- 12.3.3. Genetic Programming (GP) -- 12.4. Experimental Design -- 12.4.1. Algorithms Considered -- 12.4.2. Performance Measurement -- 12.5. Results -- 12.6. Conclusions. |