- Author: Jason Brownlee PhD
- Format: online HTML
- Price: free
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.
- Author: Thomas Weise
- Format: PDF
- Price: free
This book is about global optimization algorithms, which are methods to find optimal solutions for given problems. It especially focuses on evolutionary computation by discussing evolutionary algorithms, genetic algorithms, genetic programming, learning classifier systems, evolution strategy, differential evolution, particle swarm optimization, and ant colony optimization.
The book also elaborates on other meta-heuristics, such as simulated annealing, hill climbing, tabu search, and random optimization.
According to the author, the book is an ongoing work in progress that has existed for over two years and has been updated and extended throughout that time. However, the book will never be finished since there is always something new to add.
- Evolutionary Algorithms
- Genetic Algorithms
- Genetic Programming
- Evolution Strategy
- Evolutionary Programming
- Learning Classifier Systems
- Hill Climbing
- Random Optimization
- Simulated Annealing
- Tabu Search
- Ant Colony Optimization
- Particle Swarm Optimization
- Memetic Algorithms
- State Space Search
- Parallelization and Distribution
- Maintaining the Optimal Set
- Benchmarks and Toy Problems
- Real-World Applications
- Research Applications
- Sigoa – Implementation in Java
- Set Theory
- Stochastic Theory
- Theoretical Computer Science
- Author: Mark Watson
- Format: archived PDF and example code
- Price: free
This Open Content book covers AI programming techniques using Java.
The latest version has a completed new chapter on statistical natural language processing and a new section on embedded expert systems, and a new chapter on spam detection.
This is not the original book written for Morgan Kaufman Publishers. This book contains all new material.
- Search – graph search, and alpha-beta search in tic-tac-toe and chess
- Natural Language Processing – a simple ATN parser that uses a huge lexicon derived from Wordnet data, material NLBean project, and an embedded Prolog parser (includes Sieuwert van Otterloo’s fine Prolog implementation in Java).
- Expert systems – two simple examples using the Jess system
- Genetic algorithms – Java utility classes and two examples
- Neural Networks – utility classes for Hopfield and back propagation. Only includes simple examples to show how to use the utility classes.
- Statistical Natural Language Processing (Markov Models)
- SPAM Email detection
Download book here