Do you need some help getting started with Genetic Algorithms, Neural Networks or Swarm Intelligence?
Implementing an algorithm can be frustrating. The algorithm descriptions are incomplete, inconsistent and distributed across academic papers, websites and code.
There are so many algorithms to choose from, it can feel overwhelming.
You need a handbook of algorithm recipes where each algorithm is described in a consistent and structured way. You need:
Clever Algorithms: Nature-Inspired Programming Recipes
Clever Algorithms is a handbook of recipes for computational problem solving algorithms from the sub-fields of Artificial Intelligence such as Computational Intelligence, Biologically Inspired Computation, and Metaheuristics.
This 438-page PDF book contains:
- 45 algorithm descriptions
- Best practice usage heuristics for each algorithm
- Pseudo-code listing of each algorithm
- Code listings of each algorithm in Ruby (source code files included)
- References for further reading including the primary sources for each algorithm
The book includes an introduction to artificial intelligence and related fields as well as advanced topics like algorithm testing and visualization. The 45 algorithms are presented in groups, as follows:
- Stochastic Algorithms: Random Search, Adaptive Random Search, Stochastic Hill Climbing, Iterated Local Search, Guided Local Search, Variable Neighborhood Search, GRASP, Scatter Search, Tabu Search and Reactive Tabu Search.
- Evolutionary Algorithms: Genetic Algorithm, Genetic Programming, Evolution Strategies, Differential Evolution, Evolutionary Programming, Grammatical Evolution, Gene Expression Programming, Learning Classifier System, NSGA and SPEA.
- Physical Algorithms: Simulated Annealing, Extremal Optimization, Harmony Search, Cultural Algorithm and the Memetic Algorithm
- Probabilistic Algorithms: PIBL, UMDA, Compact Genetic Algorithm, Bayesian Optimization Algorithm and the Cross-Entropy Method.
- Swarm Algorithms: Particle Swarm Optimization, Ant System, Ant Colony Optimization, Bees Algorithm and the Bacterial Foraging Optimization Algorithm.
- Immune Algorithms: Clonal Selection Algorithm, Negative Selection Algorithm, Artificial Immune Recognition System, Immune Network Algorithm and the Dendritic Cell Algorithm.
- Neural Algorithms: Perceptron, Back-Propagation, Hopfield Network, Learning Vector Quantization and the Self-Organizing Map.
All algorithm descriptions include a working implementation in Ruby and the standalone ruby files for each algorithm are included with the book.