Parallel Problem Solving from Nature, PPSN XI: 11th International Conference, Krakov, Poland, September 11-15, 2010, Proceedings, Part I (Lecture Notes in Computer Science, 6238, Band 6238) - Livres de poche

EAN (ISBN-13): 9783642158438
ISBN (ISBN-10): 3642158439
Version reliée
Livre de poche
Date de parution: 2010
Editeur: Schaefer, Robert, Cotta, Carlos, Kolodziej, Joanna, Rudolph, Günter, Springer
742 Pages
Poids: 1,071 kg
Langue: eng/Englisch

Livre dans la base de données depuis 2009-03-19T23:39:05+01:00 (Paris)
Page de détail modifiée en dernier sur 2024-01-31T14:35:45+01:00 (Paris)
ISBN/EAN: 9783642158438

ISBN - Autres types d'écriture:
3-642-15843-9, 978-3-642-15843-8
Autres types d'écriture et termes associés:
Auteur du livre: carlo schaefer, rudolph udo, cotta, günter schäfer, carlos ott, robert carl, kolodziej, birmingham, odziej, paris review
Titre du livre: problem solving, parallel, september, proceedings from, our nature, nature international science, lecture notes computer science, international conference proceedings

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Données de l'éditeur

Auteur: Robert Schaefer; Carlos Cotta; Joanna Kolodziej; Günter Rudolph
Titre: Lecture Notes in Computer Science; Theoretical Computer Science and General Issues; Parallel Problem Solving from Nature, PPSN XI - 11th International Conference, Krakov, Poland, September 11-15, 2010, Proceedings, Part I
Editeur: Springer; Springer Berlin
742 Pages
Date de parution: 2010-09-03
Berlin; Heidelberg; DE
Langue: Anglais
106,99 € (DE)
109,99 € (AT)
118,00 CHF (CH)
Available
XXI, 742 p. 211 illus.

BC; Hardcover, Softcover / Biologie/Sonstiges; DV-gestützte Biologie/Bioinformatik; Verstehen; algorithms; data mining; evolution; evolutionary algorithm; evolutionary computation; genetic algorithms; image processing; intelligence; learning; machine learning; modeling; multi-agent system; multi-objective optimization; problem solving; swarm intell; algorithm analysis and problem complexity; Computational and Systems Biology; Artificial Intelligence; Theory of Computation; Algorithms; Automated Pattern Recognition; Discrete Mathematics in Computer Science; Künstliche Intelligenz; Theoretische Informatik; Algorithmen und Datenstrukturen; Mustererkennung; Mathematik für Informatiker; Diskrete Mathematik; EA

Theory of Evolutionary Computing (I).- Optimal Fixed and Adaptive Mutation Rates for the LeadingOnes Problem.- Mirrored Sampling and Sequential Selection for Evolution Strategies.- Optimisation and Generalisation: Footprints in Instance Space.- Adaptive Drift Analysis.- Optimizing Monotone Functions Can Be Difficult.- Log-Linear Convergence of the Scale-Invariant (?/? w ,?)-ES and Optimal ? for Intermediate Recombination for Large Population Sizes.- Exploiting Overlap When Searching for Robust Optima.- Benchmarking Evolutionary Algorithms: Towards Exploratory Landscape Analysis.- One-Point Geometric Crossover.- When Does Dependency Modelling Help? Using a Randomized Landscape Generator to Compare Algorithms in Terms of Problem Structure.- First-Improvement vs. Best-Improvement Local Optima Networks of NK Landscapes.- Differential Mutation Based on Population Covariance Matrix.- General Lower Bounds for the Running Time of Evolutionary Algorithms.- A Binary Encoding Supporting Both Mutation and Recombination.- Towards Analyzing Recombination Operators in Evolutionary Search.- Theory of Evolutionary Computing (II).- Bidirectional Relation between CMA Evolution Strategies and Natural Evolution Strategies.- A Fine-Grained View of GP Locality with Binary Decision Diagrams as Ant Phenotypes.- Drift Analysis with Tail Bounds.- More Effective Crossover Operators for the All-Pairs Shortest Path Problem.- Comparison-Based Adaptive Strategy Selection with Bandits in Differential Evolution.- Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutation.- An Archive Maintenance Scheme for Finding Robust Solutions.- Experimental Supplements to the Theoretical Analysis of Migration in the Island Model.- General Scheme for Analyzing RunningTimes of Parallel Evolutionary Algorithms.- Negative Drift in Populations.- Log(?) Modifications for Optimal Parallelism.- The Linkage Tree Genetic Algorithm.- An Analysis of the XOR Dynamic Problem Generator Based on the Dynamical System.- The Role of Degenerate Robustness in the Evolvability of Multi-agent Systems in Dynamic Environments.- Machine Learning, Classifier Systems, Image Processing.- Evolutionary Learning of Technical Trading Rules without Data-Mining Bias.- Using Computational Intelligence to Identify Performance Bottlenecks in a Computer System.- Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation.- Globally Induced Model Trees: An Evolutionary Approach.- Open-Ended Evolutionary Robotics: An Information Theoretic Approach.- A Novel Similarity-Based Crossover for Artificial Neural Network Evolution.- Indirect Encoding of Neural Networks for Scalable Go.- Comparison-Based Optimizers Need Comparison-Based Surrogates.- A Cooperative Coevolutionary Approach to Partitional Clustering.- Feature Selection for Multi-purpose Predictive Models: A Many-Objective Task.- Incorporating Domain Knowledge into Evolutionary Computing for Discovering Gene-Gene Interaction.- The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies.- Threshold Selection, Mitosis and Dual Mutation in Cooperative Co-evolution: Application to Medical 3D Tomography.- Comparative Analysis of Search and Score Metaheuristics for Bayesian Network Structure Learning Using Node Juxtaposition Distributions.- Analyzing the Credit Default Swap Market Using Cartesian Genetic Programming.- Memetic Algorithms, Hybridized Techniques, Meta and Hyperheurisics.- A Memetic CooperativeOptimization Schema and Its Application to the Tool Switching Problem.- Ownership and Trade in Spatial Evolutionary Memetic Games.- A Hyper-Heuristic Approach to Strip Packing Problems.- Asymptotic Analysis of Computational Multi-Agent Systems.- Path-Guided Mutation for Stochastic Pareto Local Search Algorithms.- Scheduling English Football Fixtures over the Holiday Period Using Hyper-heuristics.- Graph Clustering Based Model Building.- How to Choose Solutions for Local Search in Multiobjective Combinatorial Memetic Algorithms.- Secure and Task Abortion Aware GA-Based Hybrid Metaheuristics for Grid Scheduling.- A Memetic Algorithm for the Pickup and Delivery Problem with Time Windows Using Selective Route Exchange Crossover.- Ant Based Hyper Heuristics with Space Reduction: A Case Study of the p-Median Problem.- A Study of Multi-parent Crossover Operators in a Memetic Algorithm.- A Hybrid Genetic Algorithm for the Traveling Salesman Problem Using Generalized Partition Crossover.- A Memetic Algorithm with Non Gradient-Based Local Search Assisted by a Meta-model.- Multiobjective Optimization, Theoretical Aspects.- Theoretically Investigating Optimal ?-Distributions for the Hypervolume Indicator: First Results for Three Objectives.- Convergence Rates of (1+1) Evolutionary Multiobjective Optimization Algorithms.- Tight Bounds for the Approximation Ratio of the Hypervolume Indicator.- Evolutionary Multiobjective Optimization Algorithm as a Markov System.- A Natural Evolution Strategy for Multi-objective Optimization.- Solving Multiobjective Optimization Problem by Constraint Optimization.- Enhancing Diversity for Average Ranking Method in Evolutionary Many-Objective Optimization.- Objective Space Partitioning Using Conflict Information for Many-Objective Optimization.- HowCrossover Speeds Up Evolutionary Algorithms for the Multi-criteria All-Pairs-Shortest-Path Problem.- Path Relinking on Many-Objective NK-Landscapes.- In Search of Equitable Solutions Using Multi-objective Evolutionary Algorithms.- Stopping Criteria for Genetic Algorithms with Application to Multiobjective Optimization.- Defining and Optimizing Indicator-Based Diversity Measures in Multiobjective Search.- On Expected-Improvement Criteria for Model-based Multi-objective Optimization.- Parameter Tuning Boosts Performance of Variation Operators in Multiobjective Optimization.
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