Category: Artificial Intelligence

Clever Algorithms: Nature-Inspired Programming Recipes

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  • 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

Global Optimization Algorithms: Theory and Application

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  • 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.

Chapters include:

  • 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
  • Contests
  • Real-World Applications
  • Research Applications
  • Sigoa – Implementation in Java
  • Set Theory
  • Stochastic Theory
  • Clustering
  • Theoretical Computer Science

Practical Artificial Intelligence Programming in Java

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  • 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.
Chapters include:

  • 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