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Learning and Intelligent Optimization

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Cover of 'Learning and Intelligent Optimization'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Multivariate Statistical Tests for Comparing Classification Algorithms
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    Chapter 2 Using Hyperheuristics under a GP Framework for Financial Forecasting
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    Chapter 3 On the Effect of Connectedness for Biobjective Multiple and Long Path Problems
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    Chapter 4 Improving Parallel Local Search for SAT
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    Chapter 5 Variable Neighborhood Search for the Time-Dependent Vehicle Routing Problem with Soft Time Windows
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    Chapter 6 Solving the Two-Dimensional Bin Packing Problem with a Probabilistic Multi-start Heuristic
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    Chapter 7 Genetic Diversity and Effective Crossover in Evolutionary Many-objective Optimization
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    Chapter 8 An Optimal Stopping Strategy for Online Calibration in Local Search
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    Chapter 9 Analyzing the Effect of Objective Correlation on the Efficient Set of MNK-Landscapes
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    Chapter 10 Instance-Based Parameter Tuning via Search Trajectory Similarity Clustering
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    Chapter 11 Effective Probabilistic Stopping Rules for Randomized Metaheuristics: GRASP Implementations
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    Chapter 12 A Classifier-Assisted Framework for Expensive Optimization Problems: A Knowledge-Mining Approach
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    Chapter 13 Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion
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    Chapter 14 Hierarchical Hidden Conditional Random Fields for Information Extraction
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    Chapter 15 Solving Extremely Difficult MINLP Problems Using Adaptive Resolution Micro-GA with Tabu Search
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    Chapter 16 Adaptive Abnormality Detection on ECG Signal by Utilizing FLAC Features
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    Chapter 17 Gravitational Interactions Optimization
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    Chapter 18 On the Neutrality of Flowshop Scheduling Fitness Landscapes
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    Chapter 19 A Reinforcement Learning Approach for the Flexible Job Shop Scheduling Problem
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    Chapter 20 Supervised Learning Linear Priority Dispatch Rules for Job-Shop Scheduling
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    Chapter 21 Fine-Tuning Algorithm Parameters Using the Design of Experiments Approach
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    Chapter 22 MetaHybrid: Combining Metamodels and Gradient-Based Techniques in a Hybrid Multi-Objective Genetic Algorithm
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    Chapter 23 Designing Stream Cipher Systems Using Genetic Programming
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    Chapter 24 GPU-Based Multi-start Local Search Algorithms
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    Chapter 25 Active Learning of Combinatorial Features for Interactive Optimization
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    Chapter 26 A Genetic Algorithm Hybridized with the Discrete Lagrangian Method for Trap Escaping
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    Chapter 27 Greedy Local Improvement of SPEA2 Algorithm to Solve the Multiobjective Capacitated Transshipment Problem
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    Chapter 28 Hybrid Population-Based Incremental Learning Using Real Codes
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    Chapter 29 Pareto Autonomous Local Search
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    Chapter 30 Transforming Mathematical Models Using Declarative Reformulation Rules
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    Chapter 31 Learning Heuristic Policies – A Reinforcement Learning Problem
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    Chapter 32 Continuous Upper Confidence Trees
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    Chapter 33 Towards an Intelligent Non-stationary Performance Prediction of Engineering Systems
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    Chapter 34 Local Search for Constrained Financial Portfolio Selection Problems with Short Sellings
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    Chapter 35 Clustering of Local Optima in Combinatorial Fitness Landscapes
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    Chapter 36 Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study
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    Chapter 37 Learning and Intelligent Optimization
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    Chapter 38 Distribution of Computational Effort in Parallel MOEA/D
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    Chapter 39 Multi Objective Genetic Programming for Feature Construction in Classification Problems
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    Chapter 40 Sequential Model-Based Optimization for General Algorithm Configuration
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    Chapter 41 Generalising Algorithm Performance in Instance Space: A Timetabling Case Study
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    Chapter 42 A Hybrid Fish Swarm Optimisation Algorithm for Solving Examination Timetabling Problems
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    Chapter 43 The Sandpile Mutation Operator for Genetic Algorithms
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    Chapter 44 Self-adaptation Techniques Applied to Multi-Objective Evolutionary Algorithms
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    Chapter 45 Analysing the Performance of Different Population Structures for an Agent-Based Evolutionary Algorithm
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    Chapter 46 EDACC - An Advanced Platform for the Experiment Design, Administration and Analysis of Empirical Algorithms
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    Chapter 47 HAL: A Framework for the Automated Analysis and Design of High-Performance Algorithms
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    Chapter 48 Hyperion – A Recursive Hyper-Heuristic Framework
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    Chapter 49 The Cross-Domain Heuristic Search Challenge – An International Research Competition
Attention for Chapter 40: Sequential Model-Based Optimization for General Algorithm Configuration
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Chapter title
Sequential Model-Based Optimization for General Algorithm Configuration
Chapter number 40
Book title
Learning and Intelligent Optimization
Published by
Springer, Berlin, Heidelberg, January 2011
DOI 10.1007/978-3-642-25566-3_40
Book ISBNs
978-3-64-225565-6, 978-3-64-225566-3
Authors

Frank Hutter, Holger H. Hoos, Kevin Leyton-Brown, Hutter, Frank, Hoos, Holger H., Leyton-Brown, Kevin

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1,068 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 <1%
Germany 5 <1%
Japan 5 <1%
Switzerland 3 <1%
Austria 3 <1%
France 2 <1%
Norway 1 <1%
Finland 1 <1%
United Kingdom 1 <1%
Other 6 <1%
Unknown 1032 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 272 25%
Student > Master 206 19%
Researcher 124 12%
Student > Bachelor 87 8%
Student > Doctoral Student 42 4%
Other 117 11%
Unknown 220 21%
Readers by discipline Count As %
Computer Science 513 48%
Engineering 146 14%
Mathematics 34 3%
Physics and Astronomy 20 2%
Chemistry 18 2%
Other 91 9%
Unknown 246 23%