Free eBook: Clever Algorithms

The book „Clever Algorithms: Nature-Inspired Programming Recipes“ by Jason Brownlee PhD 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.

5 Reasons To Read:

  1. 45 algorithms described.
  2. Designed specifically for Programmers, Research Scientists and Interested Amateurs.
  3. Complete code examples in the Ruby programming language.
  4. Standardized algorithm descriptions.
  5. Algorithms drawn from the popular fields of Computational Intelligence, Metaheuristics, and Biologically Inspired Computation.

Table of Contents

  1. Background
    1. Introduction: What is AI, Problem Domains, Unconventional Optimization, Book Organization, How to Read this Book, Further Reading
  2. Algorithms
    1. 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.
    2. 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.
    3. Physical Algorithms: Simulated Annealing, Extremal Optimization, Harmony Search, Cultural Algorithm, Memetic Algorithm.
    4. Probabilistic Algorithms: Population-Based Incremental Learning, Univariate Marginal Distribution Algorithm, Compact Genetic Algorithm, Bayesian Optimization Algorithm, Cross-Entropy Method.
    5. Swarm Algorithms: Particle Swarm Optimization, Ant System, Ant Colony System, Bees Algorithm, Bacterial Foraging Optimization Algorithm.
    6. Immune Algorithms: Clonal Selection Algorithm, Negative Selection Algorithm, Artificial Immune Recognition System, Immune Network Algorithm, Dendritic Cell Algorithm.
    7. Neural Algorithms: Perceptron, Back-Propagation, Hopfield Network, Learning Vector Quantization, Self-Organizing Map.
  3. Extensions
    1. Advanced Topics: Programming Paradigms, Devising New Algorithms, Testing Algorithms, Visualizing Algorithms, Problem Solving Strategies, Benchmarking Algorithms
  4. Appendix
    1. Ruby: Quick-Start Guide