Clever Algorithms

书名：Clever AlgorithmsNature-InspiredProgrammingRecipes
作者：JasonBrownlee
译者：
ISBN：9781446785065
出版社：lulu.com
出版时间：2012-6-15
格式：epub/mobi/azw3/pdf
页数：438
豆瓣评分：

书籍简介：

Download : http://www.lulu.com/product/file-download/clever-algorithms-nature-inspired-programming-recipes/14696557 Read free on line:http://www.cleveralgorithms.com/nature-inspired/index.html 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. 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: 45 algorithms described. Designed specifically for Programmers, Research Scientists and Interested Amateurs. Complete code examples in the Ruby programming language. Standardized algorithm descriptions. Algorithms drawn from the popular fields of Computational Intelligence, Metaheuristics, and Biologically Inspired Computation.

作者简介：

书友短评：

@ Mars 算是工具书吧，代码写得实在是太学院派了，根本没法看。不过介绍的算法比较全面。但是都是蜻蜓点水，权当开阔视野好了。 @ 王晓辰 就是一目录书…… @ 王晓辰 就是一目录书…… @ Mars 算是工具书吧，代码写得实在是太学院派了，根本没法看。不过介绍的算法比较全面。但是都是蜻蜓点水，权当开阔视野好了。

Table of Contents
copyright
foreword
preface
acknowledgments
Background
Introduction
What is AI
Problem Domains
Unconventional Optimization
Book Organization
How to Read this Book
Further Reading
Algorithms
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
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
Physical Algorithms
Simulated Annealing
Extremal Optimization
Harmony Search
Cultural Algorithm
Memetic Algorithm
Probabilistic Algorithms
Population-Based Incremental Learning
Univariate Marginal Distribution Algorithm
Compact Genetic Algorithm
Bayesian Optimization Algorithm
Cross-Entropy Method
Swarm Algorithms
Particle Swarm Optimization
Ant System
Ant Colony System
Bees Algorithm
Bacterial Foraging Optimization Algorithm
Immune Algorithms
Clonal Selection Algorithm
Negative Selection Algorithm
Artificial Immune Recognition System
Immune Network Algorithm
Dendritic Cell Algorithm
Neural Algorithms
Perceptron
Back-propagation
Hopfield Network
Learning Vector Quantization
Self-Organizing Map
Extensions
Advanced Topics
Programming Paradigms
Devising New Algorithms
Testing Algorithms
Visualizing Algorithms
Problem Solving Strategies
Benchmarking Algorithms
Appendix A – Ruby: Quick-Start Guide
Overview
Language Basics
Ruby Idioms
Errata
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