Bilaketaren emaitzak - "evolutionary computation"

Advances in computational particle based methods /

Gaiak: “...Evolutionary computation....”
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Applications of multi-objective evolutionary algorithms

Gaiak: “...Evolutionary computation....”
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Evolutionary optimization algorithms biologically-Inspired and population-based approaches to computer intelligence / nork Simon, Dan, 1960-

Gaiak: “...Evolutionary computation....”
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Evolutionary algorithms for mobile ad hoc networks / nork Dorronsoro, Bernabé

Gaiak: “...Evolutionary computation....”
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Genetic and evolutionary computation medical applications /

Aurkibidea: “...Evolutionary computation : a brief overview -- A review of medical applications of genetic and evolutionary computation -- Evolutionary deformable models for medical image segmentation / Chris McIntosh and Ghassan Hamarneh -- Feature selection for the classification of microcalcifications in digital mammograms using genetic algorithms, sequential search, and class separability / Santiago E. ...”
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Artificial life and evolutionary computation proceedings of Wivace 2008, Venice, Italy, 8-10 September 2008 /

Gaiak: “...Evolutionary computation Congresses....”
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Evolutionary computation in gene regulatory network research /

Gaiak: “...Evolutionary computation....”
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A-Life for music : music and computer models of living systems /

Gaiak: “...Evolutionary computation....”
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Fundamentals of computational intelligence : neural networks, fuzzy systems, and evolutionary computation / nork Keller, James M., Liu, Derong, Fogel, David B.

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Computational intelligence and its applications evolutionary computation, fuzzy logic, neural network and support vector machine techniques /

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Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing / nork Siddique, N. H.

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Advances in data-based approaches for hydrologic modeling and forecasting

Aurkibidea: “...Setting the stage -- Stochastic methods for modeling precipitation and streamflow -- Model calibration in watershed hydrology -- Scaling and fractals in hydrology -- Remote sensing for precipitation and hydrologic applications -- Nearly two decades of neural network hydrologic modeling -- Evolutionary computing in hydrology -- Wavelet analyses in hydrology -- Nonlinear dynamics and chaos in hydrology -- Summary and future....”
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Multicriteria decision aid and artificial intelligence links, theory and applications / nork Doumpos, Michael

Aurkibidea: “...Machine generated contents note: List of Contributors Preface Part One The Contributions of Intelligent Techniques in Multicriteria Decision Aiding 1 Computational Intelligence Techniques for Multicriteria Decision Aiding: An Overview 1.1 Introduction 1.2 The MCDA Paradigm 1.2.1 Modeling Process 1.2.2 Methodological Approaches 1.3 Computational Intelligence in MCDA 1.3.1 Statistical Learning and Data Mining 1.3.2 Fuzzy Modeling 1.3.3 Metaheuristics 1.4 Conclusions References 2 Intelligent Decision Support Systems 2.1 Introduction 2.2 Fundamentals of Human Decision Making 2.3 Decision Support System 2.4 Intelligent Decision Support Systems 2.4.1 Artificial Neural Networks for Intelligent Decision Support 2.4.2 Fuzzy Logic for Intelligent Decision Support 2.4.3 Expert Systems for Intelligent Decision Support 2.4.4 Evolutionary Computing for Intelligent Decision Support 2.4.5 Intelligent Agents for Intelligent Decision Support 2.5 Evaluating Intelligent Decision Support Systems 2.5.1 Determining Evaluation Criteria 2.5.2 Multi-Criteria Model for IDSS Assessment 2.6 Summary and Future Trends References Part Two Intelligent Technologies for Decision Support and Preference Modeling 3 Designing Distributed Multi-Criteria Decision Support Systems for Complex and Uncertain Situations 3.1 Introduction 3.2 Example Applications 3.3 Key Challenges 3.4 Making Trade-offs: Multi-criteria Decision Analysis 3.4.1 Multi-attribute Decision Support 3.4.2 Making Trade-offs Under Uncertainty 3.5 Exploring the Future: Scenario-based Reasoning 3.6 Making Robust Decisions: Combining MCDA and SBR 3.6.1 Decisions Under Uncertainty: The Concept of Robustness 3.6.2 Combining Scenarios and MCDA 3.6.3 Collecting, Sharing and Processing Information: A Distributed Approach 3.6.4 Keeping Track of Future Developments: Constructing Comparable Scenarios 3.6.5 Respecting Constraints and Requirements: Scenario Management 3.6.6 Assisting Evaluation: Assessing Large Numbers of Scenarios 3.7 Discussion 3.8 Conclusion References 4 Preference Representation with Ontologies 4.1 Introduction 4.1.1 Structure of the Chapter 4.2 Ontology-based Preference Models 4.3 Maintaining the User's Profile up to Date 4.4 Decision Making Methods Exploiting the Preference Information Stored in Ontologies 4.4.1 Recommendation Based on Aggregation 4.4.2 Recommendation Based on Similarities 4.4.3 Recommendation Based on Rules 4.5 Discussion and Open Questions References Part Three Decision Models 5 Neural Networks in Multicriteria Decision Support 5.1 Introduction 5.2 Basic Concepts of Neural Networks 5.2.1 Neural Networks for Intelligent Decision Support 5.3 Basics in Multicriteria Decision Aid 5.3.1 MCDM Problems 5.3.2 Solutions of MCDM Problems 5.4 Neural Networks and Multicriteria Decision Support 5.4.1 Review of Neural Network Applications to MCDM Problems 5.4.2 Discussion 5.5 Summary and Conclusions References 6 Rule-Based Approach to Multicriteria Ranking 6.1 Introduction 6.2 Problem Setting 6.3 Pairwise Comparison Table (PCT) 6.4 Rough Approximation of Outranking and Non-outranking Relations 6.5 Induction and Application of Decision Rules 6.6 Exploitation of Preference Graphs 6.7 Illustrative Example 6.8 Summary and Conclusions References 7 About the Application of Evidence Theory in MultiCriteria Decision Aid 7.1 Introduction 7.2 Evidence Theory: Some Concepts 7.2.1 Knowledge Model 7.2.2 Combination 7.2.3 Decision Making 7.3 New Concepts in Evidence Theory for MCDA 7.3.1 First Belief Dominance 7.3.2 RBBD Concept 7.4 Multicriteria Methods modeled by Evidence Theory 7.4.1 Evidential Reasoning Approach 7.4.2 DS/AHP 7.4.3 DISSET 7.4.4 A Choice Model Inspired by ELECTRE I 7.4.5 A Ranking Model Inspired by Xu et al.'...”
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