5 Reasons To Read:
- 45 algorithms described.
- Designed specifically for Programmers, Research Scientists and Interested Amateurs.
- Complete code examples in the Ruby programming language.
- Standardized algorithm descriptions.
- Algorithms drawn from the popular fields of Computational Intelligence, Metaheuristics, and Biologically Inspired Computation.
Table of Contents
- Introduction: What is AI, Problem Domains, Unconventional Optimization, Book Organization, How to Read this Book, Further Reading
- 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.
- Advanced Topics: Programming Paradigms, Devising New Algorithms, Testing Algorithms, Visualizing Algorithms, Problem Solving Strategies, Benchmarking Algorithms
- Ruby: Quick-Start Guide