Lecture Notes in Computer Science - 1917: Parallel Problem Solving from Nature PPSN VI: 6th International Conference, Paris, France, September 18-20, 2000; proceedings Marc Schoenauer

Marc Schoenauer

ISBN 10: 3540410562 / ISBN 13: 9783540410560

Editore: Springer, 2000

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Lecture Notes in Computer Science - 1917: Parallel Problem Solving from Nature PPSN VI: 6th International Conference, Paris, France, September 18-20, 2000; proceedings In deutscher Sprache. pages.

Codice articolo BN543298

Dati bibliografici

• Titolo: Lecture Notes in Computer Science - 1917: Parallel Problem Solving from Nature PPSN VI: 6th International Conference, Paris, France, September 18-20, 2000; proceedings Marc Schoenauer
• Editore: Springer
• Data di pubblicazione: 2000
• Lingua: Inglese
• ISBN 10: 3540410562
• ISBN 13: 9783540410560
• Rilegatura: Brossura
• Edizione: 2000.
• Condizione: gut
• Peso: 2000 grammi

Descrizione articolo

Riassunto

We are proud to introduce the proceedings of the Sixth International Conference on Parallel Problem Solving from Nature, PPSN VI, held in Paris, Prance, on 18-20 September 2000. PPSN VI was organized in association with the Genetic and Evolutionary Computing Conference (GECCO'2000) and the Congress on Evolutionary Computation (CEC'2000), reflecting the beneficial interaction between the conference activities in Europe and in the USA in the field of natural computation. Starting in 1990 in Dortmund, Germany (Proceedings, LNCS vol. 496, Sprin­ ger, 1991), this biannual meeting has been held in Brussels, Belgium (Procee­ dings, Elsevier, 1992), Jerusalem, Israel (Proceedings, LNCS vol. 866, Springer, 1994), Berlin, Germany (Proceedings, LNCS vol. 1141, Springer, 1996), and Amsterdam, The Netherlands (Proceedings, LNCS vol. 1498, Springer, 1998), where it was decided that Paris would be the location of the 2000 conference with Marc Schoenauer as the general chair. The scientific content of the PPSN conference focuses on problem solving pa­ radigms gleaned from a natural models. Characteristic for Natural Computing is the metaphorical use of concepts, principles and mechanisms underlying natural systems, such as evolutionary processes involving mutation, recombination, and selection in natural evolution, annealing or punctuated equilibrium processes of many-particle systems in physics, growth processes in nature and economics, collective intelligence in biology, DNA-based computing in molecular chemistry, and multi-cellular behavioral processes in neural and immune networks.

Contenuti

Invited Papers.- Interacting Trajectories in Design Space and Niche Space: A Philosopher Speculates About Evolution.- Language as a Complex Adaptive System.- Analysis and Theory of EAs.- Cellular Evolutionary Algorithms: Evaluating the Influence of Ratio.- Efficiency and Mutation Strength Adaptation of the (?/? I, ?)-ES in a Noisy Environment.- An Analysis of the Configuration Space of the Maximal Constraint Satisfaction Problem.- On the Desired Behaviors of Self-Adaptive Evolutionary Algorithms.- Practical Implications of New Results in Conservation of Optimizer Performance.- Large Deviations, Evolutionary Computation and Comparisons of Algorithms.- On the Choice of the Mutation Probability for the (1+1) EA.- The Genetic Code-Like Transformations and Their Effect on Learning Functions.- Perturbation Theory for Evolutionary Algorithms: Towards an Estimation of Convergence Speed.- Statistical Characteristics of Evolution Strategies.- Consensus Sequence Plots and Error Thresholds: Tools for Visualising the Structure of Fitness Landscapes.- Experiments with Tuneable Fitness Landscapes.- Introducing a New Persistence Measure.- An Analysis of Dynamic Severity and Population Size.- Functions as Permutations: Regarding No Free Lunch, Walsh Analysis and Summary Statistics.- Genetic Programming.- Distributed Hybrid Genetic Programming for Learning Boolean Functions.- Genetic Programming with Dynamic Fitness for a Remote Sensing Application.- Genetic Programming Bloat without Semantics.- Genetic Programming and Domain Knowledge: Beyond the Limitations of Grammar-Guided Machine Discovery.- Polymorphy and Hybridization in Genetically Programmed Networks.- Building Optimal Committees of Genetic Programs.- Scheduling.- Distributed Simulated Annealing for Job Shop Scheduling.- Anticipation in Dynamic Optimization: The Scheduling Case.- Multirecombinated Evolutionary Algorithms for the Flow Shop Scheduling Problem.- GA Based on the UV-Structure Hypothesis and Its Application to JSP.- Neighbourhood Based Robustness Applied to Tardiness and Total Flowtime Job Shops.- Solving Extended Hybrid-Flow-Shop Problems Using Active Schedule Generation and Genetic Algorithms.- A Comparison of Genetic Algorithms for the Static Job Shop Scheduling Problem.- Representations and Operators.- An Emperical Study on GAs “Without Parameters”.- Using Dynastic Exploring Recombination to Promote Diversity in Genetic Search.- Adaptive Control of the Mutation Probability by Fuzzy Logic Controllers.- A Comparison of Two Representations for the Fixed Charge Transportation Problem.- Invariance, Self-Adaptation and Correlated Mutations in Evolution Strategies.- Theoretical Analysis of Simplex Crossover for Real-Coded Genetic Algorithms.- Applying Self-Organised Criticality to Evolutionary Algorithms.- Genetic Algorithms, Clustering, and the Breaking of Symmetry.- Pruefer Numbers and Genetic Algorithms: A Lesson on How the Low Locality of an Encoding Can Harm the Performance of Gas.- Median-Selection for Parallel Steady-State Evolution Strategies.- The Origination of Diversity by Adaptive Clustering.- Symbiotic Combination as an Alternative to Sexual Recombination in Genetic Algorithms.- Co-evolution.- Island Model Cooperating with Speciation for Multimodal Optimization.- Optimizing through Co-evolutionary Avalanches.- Evolution of Altruism in Viscous Populations: Effects of Altruism on the Evolution of Migrating Behavior.- A Game-Theoretic Approach to the Simple Coevolutionary Algorithm.- The Number of People with Whom a Man Interacts.- NK-landscapes as Test Functions for Evaluation of Host-Parasite Algorithms.- Towards Balanced Coevolution.- Spatial Games with Adaptive Tit-for-Tats.- Competitive Segmentation: A Struggle for Image Space.- Constraint Handling Techniques.- An Adaptive Algorithm for Constrained Optimization Problems.- Test-Case Generator TCG-2 for Nonlinear Parameter Optimisation.- Solving CSP Instances Beyond the Phase Transition Using Stochastic Search Algorithms.- Noisy and Non-stationary Environments.- Steady-State Evolutionary Path Planning, Adaptive Replacement, and Hyper-Diversity.- Optimization of Noisy Fitness Functions by Means of Genetic Algorithms Using History of Search.- Evolvable Hardware and Hardware Implementation of EAs.- An Efficient Random Number Generation Architecture for Hardware Parallel Genetic Algorithms.- An Integrated On-Line Learning System for Evolving Programmable Logic Array Controllers.- Combinatorial Optimisation.- Selection and Reinforcement Learning for Combinatorial Optimization.- Ant Colony Optimization for the Total Weighted Tardiness Problem.- Adaptive Fitness Functions for the Satisfiability Problem.- Large-Scale Permutation Optimization with the Ordering Messy Genetic Algorithm.- A Hybrid GA for the Edge-Biconnectivity Augmentation Problem.- A Temporal Representation for GA and TSP.- A Comparison of Nature Inspired Heuristics on the Traveling Salesman Problem.- A Genetic Algorithm for VLSI Floorplanning.- Applications.- Scalability and Efficiency of Genetic Algorithms for Geometrical Applications.- Genetic Optimization of the EPR Spectral Parameters: Algorithm Implementation and Preliminary Results.- Fitting Fluorescence Spectra with Genetic Algorithms.- Real-Coded Adaptive Range Genetic Algorithm Applied to Transonic Wing Optimization.- Stream Cyphers with One- and Two-Dimensional Cellular Automata.- Machine Learning and Classifier Systems.- Investigating Generalization in the Anticipatory Classifier System.- A New Bootstrapping Method to Improve Classification Performance in Learning Classifier Systems.- Towards Automatic Domain Knowledge Extraction for Evolutionary Heuristics.- New Algorithms and Metaphors.- Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The ID A.- A New Genetic Algorithms Working on State Domain Order Statistics.- A Factorized Distribution Algorithm Using Single Connected Bayesian Networks.- Optimization as Side-Effect of Evolving Allelopathic Diversity.- Reaction-Diffusion Model of a Honeybee Colony’s Foraging Behaviour.- A Religion-Based Spatial Model for Evolutionary Algorithms.- Bayesian Evolutionary Optimization Using Helmholtz Machines.- Multiobjective Optimisation.- The Pareto Envelope-Based Selection Algorithm for Multiobjective Optimization.- A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II.- Mechanical Component Design for Multiple Ojectives Using Elitist Non-dominated Sorting GA.- On the Assessment of Multiobjective Approaches to the Adaptive Distributed Database Management Problem.- A Hierarchical Genetic Algorithm Using Multiple Models for Optimization.- EA Software.- Take It EASEA.- Evolutionary Computation Visualization: Application to G-PROP.