Operations Research/Computer Science Interfaces

By: Roberto Battiti, Mauro Brunato, Franco Mascia

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Hardcover | 28 November 2008

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210 Pages

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23.5 x 15.88 x 1.27

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Reactive Search and Intelligent Optimization is an excellent introduction to the main principles of reactive search, as well as an attempt to develop some fresh intuition for the approaches. The book looks at different optimization possibilities with an emphasis on opportunities for learning and self-tuning strategies. While focusing more on methods than on problems, problems are introduced wherever they help make the discussion more concrete, or when a specific problem has been widely studied by reactive search and intelligent optimization heuristics.

Individual chapters cover reacting on the neighborhood; reacting on the annealing schedule; reactive prohibitions; model-based search; reacting on the objective function; relationships between reactive search and reinforcement learning; and much more. Each chapter is structured to show basic issues and algorithms; the parameters critical for the success of the different methods discussed; and opportunities for the automated tuning of these parameters.

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Published: 6th December 2010

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Introduction: Machine Learning for Intelligent Optimization	p. 1
Parameter Tuning and Intelligent Optimization	p. 4
Book Outline	p. 7
Reacting on the Neighborhood	p. 9
Local Search Based on Perturbations	p. 9
Learning How to Evaluate the Neighborhood	p. 13
Learning the Appropriate Neighborhood in Variable Neighborhood Search	p. 14
Iterated Local Search	p. 18
Reacting on the Annealing Schedule	p. 25
Stochasticity in Local Moves and Controlled Worsening of Solution Values	p. 25
Simulated Annealing and Asymptotics	p. 26
Asymptotic Convergence Results	p. 27
Online Learning Strategies in Simulated Annealing	p. 29
Combinatorial Optimization Problems	p. 30
Global Optimization of Continuous Functions	p. 33
Reactive Prohibitions	p. 35
Prohibitions for Diversification	p. 35
Forms of Prohibition-Based Search	p. 36
Dynamical Systems	p. 37
A Worked-Out Example of Fixed Tabu Search	p. 39
Relationship Between Prohibition and Diversification	p. 39
How to Escape from an Attractor	p. 41
Reactive Tabu Search: Self-Adjusted Prohibition Period	p. 49
The Escape Mechanism	p. 51
Applications of Reactive Tabu Search	p. 51
Implementation: Storing and Using the Search History	p. 52
Fast Algorithms for Using the Search History	p. 54
Persistent Dynamic Sets	p. 54
Reacting on the Objective Function	p. 59
Dynamic Landscape Modifications to Influence Trajectories	p. 59
Adapting Noise Levels	p. 62
Guided Local Search	p. 63
Eliminating Plateaus by Looking Inside the Problem Structure	p. 66
Nonoblivious Local Search for SAt	p. 66
Model-Based Search	p. 69
Models of a Problem	p. 69
An Example	p. 71
Dependent Probabilities	p. 73
The Cross-Entropy Model	p. 75
Adaptive Solution Construction with Ant Colonies	p. 77
Modeling Surfaces for Continuous Optimization	p. 79
Supervised Learning	p. 83
Learning to Optimize, from Examples	p. 83
Techniques	p. 84
Linear Regression	p. 84
Bayesian Locally Weighted Regression	p. 88
Using Linear Functions for Classification	p. 92
Multilayer Perceptrons	p. 94
Statistical Learning Theory and Support Vector Machines	p. 95
Nearest Neighbor's Methods	p. 101
Selecting Features	p. 102
Correlation Coefficient	p. 104
Correlation Ratio	p. 104
Entropy and Mutual Information	p. 105
Applications	p. 106
Learning a Model of the Solver	p. 110
Reinforcement Learning	p. 117
Reinforcement Learning Basics: Learning from a Critic	p. 117
Markov Decision Processes	p. 118
Dynamic Programming	p. 120
Approximations: Reinforcement Learning and Neuro-Dynamic Programming	p. 123
Relationships Between Reinforcement Learning and Optimization	p. 125
Algorithm Portfolios and Restart Strategies	p. 129
Introduction: Portfolios and Restarts	p. 129
Predicting the Performance of a Portfolio from its Component Algorithms	p. 130
Parallel Processing	p. 132
Reactive Portfolios	p. 134
Defining an Optimal Restart Time	p. 135
Reactive Restarts	p. 138
Racing	p. 141
Exploration and Exploitation of Candidate Algorithms	p. 141
Racing to Maximize Cumulative Reward by Interval Estimation	p. 142
Aiming at the Maximum with Threshold Ascent	p. 144
Racing for Off-Line Configuration of Metaheuristics	p. 145
Teams of Interacting Solvers	p. 151
Complex Interaction and Coordination Schemes	p. 151
Genetic Algorithms and Evolution Strategies	p. 152
Intelligent and Reactive Solver Teams	p. 156
An Example: Gossiping Optimization	p. 159
Epidemic Communication for Optimization	p. 160
Metrics, Landscapes, and Features	p. 163
How to Measure and Model Problem Difficulty	p. 163
Phase Transitions in Combinatorial Problems	p. 164
Empirical Models for Fitness Surfaces	p. 165
Tunable Landscapes	p. 168
Measuring Local Search Components: Diversification and Bias	p. 170
The Diversification-Bias Compromise (D-B Plots)	p. 173
A Conjecture: Better Algorithms are Pareto-Optimal in D-B Plots	p. 175
Open Problems	p. 177
References	p. 181
Index	p. 195
Table of Contents provided by Ingram. All Rights Reserved.

Operations Research/Computer Science Interfaces

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Hardcover

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