Clever Algorithms : Nature-Inspired Programming Recipes

Clever Algorithms -Nature-Inspired Programming Recipes

TOC
I Background
1 Introduction

2 Stochastic Algorithms
2.1 Overview
2.2 Random Search
2.3 Adaptive Random Search
2.4 Stochastic Hill Climbing
2.5 Iterated Local Search
2.6 Guided Local Search
2.7 Variable Neighborhood Search
2.8 Greedy Randomized Adaptive Search
2.9 Scatter Search
2.10 Tabu Search
2.11 Reactive Tabu Search

3 Evolutionary Algorithms
3.2 Genetic Algorithm
3.3 Genetic Programming
3.4 Evolution Strategies
3.5 Differential Evolution
3.6 Evolutionary Programming
3.7 Grammatical Evolution
3.8 Gene Expression Programming
3.9 Learning Classifier System
3.10 Non-dominated Sorting Genetic Algorithm
3.11 Strength Pareto Evolutionary Algorithm

4 Physical Algorithms
4.1 Overview
4.2 Simulated Annealing
4.3 Extremal Optimization
4.4 Harmony Search
4.5 Cultural Algorithm
4.6 Memetic Algorithm

5 Probabilistic Algorithms
5.2 Population-Based Incremental Learning
5.3 Univariate Marginal Distribution Algorithm
5.4 Compact Genetic Algorithm
5.5 Bayesian Optimization Algorithm
5.6 Cross-Entropy Method

6 Swarm Algorithms
6.2 Particle Swarm Optimization
6.3 Ant System
6.4 Ant Colony System
6.5 Bees Algorithm
6.6 Bacterial Foraging Optimization Algorithm

7 Immune Algorithms
7.2 Clonal Selection Algorithm
7.3 Negative Selection Algorithm
7.4 Artificial Immune Recognition System
7.5 Immune Network Algorithm
7.6 Dendritic Cell Algorithm

8 Neural Algorithms
8.2 Perceptron
8.3 Back-propagation
8.4 Hopfield Network
8.5 Learning Vector Quantization
8.6 Self-Organizing Map

III Extensions
9 Advanced Topics
9.1 Programming Paradigms
9.2 Devising New Algorithms
9.3 Testing Algorithms
9.4 Visualizing Algorithms
9.5 Problem Solving Strategies
9.6 Benchmarking Algorithms

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