內容簡介,翻譯,目錄,翻譯,
內容簡介
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.
翻譯
這本書提供了一個手冊的算法食譜Metaheuristics、生物啟發計算和計算智慧型中描述一個完整的、一致的和集中的方式。這些標準化的描述被精心設計的,可用的,可以理解的。大多數在這本書中描述的算法最初是受生物和自然系統的啟發,如基因進化的自適應能力和獲得性免疫系統,和鳥類的覓食行為,蜜蜂、螞蟻和細菌。一種百科全書式的算法參考,這本書是用於科研的科學家,工程師,學生,和感興趣的業餘愛好者。每個算法描述工作提供了一個Ruby程式語言中的代碼範例。
這本書“聰明的算法:產品表面編程食譜”傑森Brownlee博士描述了45從人工智慧領域的算法。算法的描述都是完整和一致的,以確保它們是可訪問,由廣大讀者可用的和可以理解的。
讀的5大理由:
45算法描述。
專門為程式設計師,研究科學家和感興趣的業餘愛好者。
完整的Ruby程式語言的代碼示例。
標準化的算法描述。
算法從流行的計算智慧型領域,Metaheuristics,生物啟發計算。
目錄
1.Background
1.1 Introduction: What is AI, Problem Domains, Unconventional Optimization, Book Organization, How to Read this Book, Further Reading
2.Algorithms
2.1 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.
2.2 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.
2.3 Physical Algorithms: Simulated Annealing, Extremal Optimization, Harmony Search, Cultural Algorithm, Memetic Algorithm.
2.4 Probabilistic Algorithms: Population-Based Incremental Learning, Univariate Marginal Distribution Algorithm, Compact Genetic Algorithm, Bayesian Optimization Algorithm, Cross-Entropy Method.
2.5 Swarm Algorithms: Particle Swarm Optimization, Ant System, Ant Colony System, Bees Algorithm, Bacterial Foraging Optimization Algorithm.
2.6 Immune Algorithms: Clonal Selection Algorithm, Negative Selection Algorithm, Artificial Immune Recognition System, Immune Network Algorithm, Dendritic Cell Algorithm.
2.7 Neural Algorithms: Perceptron, Back-Propagation, Hopfield Network, Learning Vector Quantization, Self-Organizing Map.
3.Extensions
3.1 Advanced Topics: Programming Paradigms, Devising New Algorithms, Testing Algorithms, Visualizing Algorithms, Problem Solving Strategies, Benchmarking Algorithms
4.Appendix
4.1 Ruby: Quick-Start Guide
1.1 Introduction: What is AI, Problem Domains, Unconventional Optimization, Book Organization, How to Read this Book, Further Reading
2.Algorithms
2.1 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.
2.2 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.
2.3 Physical Algorithms: Simulated Annealing, Extremal Optimization, Harmony Search, Cultural Algorithm, Memetic Algorithm.
2.4 Probabilistic Algorithms: Population-Based Incremental Learning, Univariate Marginal Distribution Algorithm, Compact Genetic Algorithm, Bayesian Optimization Algorithm, Cross-Entropy Method.
2.5 Swarm Algorithms: Particle Swarm Optimization, Ant System, Ant Colony System, Bees Algorithm, Bacterial Foraging Optimization Algorithm.
2.6 Immune Algorithms: Clonal Selection Algorithm, Negative Selection Algorithm, Artificial Immune Recognition System, Immune Network Algorithm, Dendritic Cell Algorithm.
2.7 Neural Algorithms: Perceptron, Back-Propagation, Hopfield Network, Learning Vector Quantization, Self-Organizing Map.
3.Extensions
3.1 Advanced Topics: Programming Paradigms, Devising New Algorithms, Testing Algorithms, Visualizing Algorithms, Problem Solving Strategies, Benchmarking Algorithms
4.Appendix
4.1 Ruby: Quick-Start Guide
翻譯
1.背景
1.1簡介:什麼是人工智慧、問題域,非傳統的最佳化,本組織,如何閱讀這本書,進一步閱讀
2.算法
2.1隨機算法:隨機搜尋、自適應隨機搜尋、隨機希爾攀登,疊代局部搜尋,引導本地搜尋,可變鄰域搜尋,貪婪隨機自適應搜尋、分散搜尋,禁忌搜尋、被動的禁忌搜尋。
2.2進化算法:遺傳算法、遺傳編程、進化策略、微分進化,進化編程語法進化,基因表達式編程,學習分類器系統,需求排序遺傳算法,力量帕累托進化算法。
2.3物理算法:模擬退火、極值最佳化搜尋,和諧文化算法,迷因算法。
2.4機率算法:基於增量學習,單變數邊緣分布算法,遺傳算法緊湊,貝葉斯最佳化算法,叉熵方法。
2.5蜂群算法:粒子群最佳化,螞蟻系統,蟻群系統,蜜蜂算法,細菌覓食最佳化算法。
2.6免疫算法:克隆選擇算法,負選擇算法,人工免疫識別系統,免疫網路算法,樹突狀細胞算法。
2.7神經算法:感知器神經網路,Hopfield網路,學習矢量量化、自組織映射。
3.擴展
3.1高級主題:編程範例,設計新的算法,測試算法、可視化算法,解決問題的策略,基準測試算法
4.附錄
4.1 Ruby:快速啟動指南
1.1簡介:什麼是人工智慧、問題域,非傳統的最佳化,本組織,如何閱讀這本書,進一步閱讀
2.算法
2.1隨機算法:隨機搜尋、自適應隨機搜尋、隨機希爾攀登,疊代局部搜尋,引導本地搜尋,可變鄰域搜尋,貪婪隨機自適應搜尋、分散搜尋,禁忌搜尋、被動的禁忌搜尋。
2.2進化算法:遺傳算法、遺傳編程、進化策略、微分進化,進化編程語法進化,基因表達式編程,學習分類器系統,需求排序遺傳算法,力量帕累托進化算法。
2.3物理算法:模擬退火、極值最佳化搜尋,和諧文化算法,迷因算法。
2.4機率算法:基於增量學習,單變數邊緣分布算法,遺傳算法緊湊,貝葉斯最佳化算法,叉熵方法。
2.5蜂群算法:粒子群最佳化,螞蟻系統,蟻群系統,蜜蜂算法,細菌覓食最佳化算法。
2.6免疫算法:克隆選擇算法,負選擇算法,人工免疫識別系統,免疫網路算法,樹突狀細胞算法。
2.7神經算法:感知器神經網路,Hopfield網路,學習矢量量化、自組織映射。
3.擴展
3.1高級主題:編程範例,設計新的算法,測試算法、可視化算法,解決問題的策略,基準測試算法
4.附錄
4.1 Ruby:快速啟動指南
