Table of Contents
copyright
foreword
preface
acknowledgments
Background
Introduction
What is AI
Problem Domains
Unconventional Optimization
Book Organization
How to Read this Book
Further Reading
Algorithms
Stochastic Algorithms
Random Search
Adaptive Random Search
Stochastic Hill Climbing
Iterated Local Search
Guided Local Search
Variable Neighborhood Search
Greedy Randomized Adaptive Search
Scatter Search
Tabu Search
Reactive Tabu Search
Evolutionary Algorithms
Genetic Algorithm
Genetic Programming
Evolution Strategies
Differential Evolution
Evolutionary Programming
Grammatical Evolution
Gene Expression Programming
Learning Classifier System
Non-dominated Sorting Genetic Algorithm
Strength Pareto Evolutionary Algorithm
Physical Algorithms
Simulated Annealing
Extremal Optimization
Harmony Search
Cultural Algorithm
Memetic Algorithm
Probabilistic Algorithms
Population-Based Incremental Learning
Univariate Marginal Distribution Algorithm
Compact Genetic Algorithm
Bayesian Optimization Algorithm
Cross-Entropy Method
Swarm Algorithms
Particle Swarm Optimization
Ant System
Ant Colony System
Bees Algorithm
Bacterial Foraging Optimization Algorithm
Immune Algorithms
Clonal Selection Algorithm
Negative Selection Algorithm
Artificial Immune Recognition System
Immune Network Algorithm
Dendritic Cell Algorithm
Neural Algorithms
Perceptron
Back-propagation
Hopfield Network
Learning Vector Quantization
Self-Organizing Map
Extensions
Advanced Topics
Programming Paradigms
Devising New Algorithms
Testing Algorithms
Visualizing Algorithms
Problem Solving Strategies
Benchmarking Algorithms
Appendix A - Ruby: Quick-Start Guide
Overview
Language Basics
Ruby Idioms
Errata
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