- Author: Jason Brownlee PhD
- Format: online HTML
- Price: free (hard copy available on Amazon)

The book describes 45 algorithms from the field of Artificial Intelligence. All algorithm descriptions are complete and consistent to ensure that they are accessible, usable and understandable by a wide audience.

This book provides a handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence that have been described in a complete, consistent, and centralized manner. These standardized descriptions were carefully designed to be accessible, usable, and understandable. Most of the algorithms described in this book were originally inspired by biological and natural systems, such as the adaptive capabilities of genetic evolution and the acquired immune system, and the foraging behaviors of birds, bees, ants and bacteria. An encyclopedic algorithm reference, this book is intended for research scientists, engineers, students, and interested amateurs. Each algorithm description provides a working code example in the Ruby Programming Language.

Chapters include:

- What is AI
- Problem Domains
- Unconventional Optimization
- 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
- 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
- Simulated Annealing
- Extremal Optimization
- Harmony Search
- Cultural Algorithm
- Memetic Algorithm
- Population-Based Incremental Learning
- Univariate Marginal Distribution Algorithm
- Compact Genetic Algorithm
- Bayesian Optimization Algorithm
- Cross-Entropy Method
- Particle Swarm Optimization
- Ant System
- Ant Colony System
- Bees Algorithm
- Bacterial Foraging Optimization Algorithm
- Clonal Selection Algorithm
- Negative Selection Algorithm
- Artificial Immune Recognition System
- Immune Network Algorithm
- Dendritic Cell Algorithm
- Perceptron
- Back-propagation
- Hopfield Network
- Learning Vector Quantization
- Self-Organizing Map
- Programming Paradigms
- Devising New Algorithms
- Testing Algorithms
- Visualizing Algorithms
- Problem Solving Strategies
- Benchmarking Algorithms