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Parallel Problem Solving from Nature, PPSN XI: 11th International Conference, Krakov, Poland, September 11-15, 2010, Proceedings, Part I de Robert Schaefer

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

Editat de Robert Schaefer, Carlos Cotta, Joanna Kolodziej, Günter Rudolph
en Limba Engleză Paperback – 3 sep 2010

Relevanța acestui volum pentru cercetătorii care urmăresc acreditări academice în informatică și inteligență artificială este dată de rigoarea științifică a seriei Lecture Notes in Computer Science. Volumul de față, Parallel Problem Solving from Nature, PPSN XI, reprezintă a doua parte a lucrărilor celei de-a 11-a conferințe internaționale PPSN, un eveniment de referință pentru comunitatea calculului natural. Suntem de părere că selecția strictă a celor 128 de lucrări dintr-un total de 232 de propuneri garantează un nivel calitativ înalt, esențial pentru literatura de specialitate.

Această ediție extinde cadrul propus de Artificial Evolution de Jin-Kao Hao cu date noi din perioada 2010, axându-se pe o diversitate mai mare de metaheuristici și sisteme bio-inspirate. În timp ce alte lucrări similare se concentrează pe algoritmi genetici clasici, Parallel Problem Solving from Nature, PPSN XI explorează frontierele interdependenței dintre modelarea dependențelor și structura problemelor, folosind generatoare de peisaje randomizate pentru a compara performanța algoritmilor.

Structura volumului este una tematică, debutând cu o secțiune densă dedicată teoriei calculului evolutiv. Primele capitole analizează riguros aspecte precum convergența log-liniară și ratele de mutație adaptive pentru probleme specifice (LeadingOnes), progresând apoi către analiza peisajelor de fitness și benchmarking. Această organizare permite cititorului o tranziție logică de la fundamentele matematice la aplicații complexe, precum optimizarea funcțiilor monotone și utilizarea matricelor de covarianță în mutațiile diferențiale. Editura Springer Berlin, Heidelberg menține standardul tehnic ridicat prin includerea de grafice și demonstrații matematice esențiale pentru înțelegerea limitelor inferioare ale timpului de execuție pentru algoritmii evolutivi.

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Specificații

ISBN-13: 9783642158438
ISBN-10: 3642158439
Pagini: 764
Ilustrații: XXI, 742 p. 211 illus.
Greutate: 1.07 kg
Ediția:2010
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Theoretical Computer Science and General Issues

Locul publicării:Berlin, Heidelberg, Germany

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Professional/practitioner

De ce să citești această carte

Această resursă este indispensabilă profesioniștilor și cercetătorilor din domeniul calculului bio-inspirat. Cititorul câștigă acces la metodologii de peer-review riguroase și la soluții de ultimă oră pentru probleme de optimizare complexă. Este un instrument valoros pentru cei care doresc să înțeleagă fundamentele teoretice ale inteligenței colective și să aplice algoritmi memetici în scenarii reale, oferind un avantaj competitiv în cercetarea academică și dezvoltarea de software avansat.

Cuprins

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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We are very pleased to present to you this LNCS volume, the proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010). PPSN is one of the most respected and highly regarded c- ference series in evolutionary computation, and indeed in natural computation aswell.Thisbiennialeventwas'rstheldinDortmundin1990, andtheninBr- sels (1992), Jerusalem (1994), Berlin (1996), Amsterdam (1998), Paris (2000), Granada (2002), Birmingham (2004), Reykjavik (2006) and again in Dortmund in 2008. PPSN 2010 received 232 submissions. After an extensive peer review p- cess involving more than 180 reviewers, the program committee chairs went through all the review reports and ranked the papers according to the revi- ers comments. Each paper wasevaluated by at least three reviewers.Additional reviewers from the appropriate branches of science were invoked to review into disciplinary papers. The top 128 papers were ?nally selected for inclusion in the proceedings and presentation at the conference. This represents an acceptance rate of 55%, which guarantees that PPSN will continue to be one of the c- ferences of choice for bio-inspired computing and metaheuristics researchers all over the world who value the quality over the size of a conference. The papers included in the proceedingsvolumes covera wide range of topics, fromevolutionarycomputationto swarmintelligence, frombio-inspiredcomp- ing to real-world applications. Machine learning and mathematical games s- portedbyevolutionaryalgorithmsaswellasmemetic, agent-orientedsystemsare also represented. They all are the latest and best in natural computation. The proceedings are composed of two volumes divided into nine thematic sections."