Parallel Genetic Algorithms

For many applications a randomized algorithm is either the simplest algorithm available, or the fastest, or both. This tutorial presents the basic concepts in the design and analysis of randomized algorithms. The first part of the book presents tools from probability theory and probabilistic analysis that are recurrent in algorithmic applications. Algorithmic examples are given to illustrate the use of each tool in a concrete setting. In the second part of the book, each of the seven chapters focuses on one important area of application of randomized algorithms: data structures; geometric algorithms; graph algorithms; number theory; enumeration; parallel algorithms; and on-line algorithms. A comprehensive and representative selection of the algorithms in these areas is also given. This book should prove invaluable as a reference for researchers and professional programmers, as well as for students.

Fifteen original contributions from experts in high-speed computation on multi-processor architectures, concurrent programming and parallel algorithms. Experts in high-speed computation agree that the

內容介紹：在 ChatGPT 帶起生成式 AI 百家齊放的新世紀, 整合語言模型已勢不可擋。隨著 OpenAI 釋出更厲害的新模型與整合度更高的 API, 善用語言模型理解文意與能言善道的超強特性, 邁入『自然語言就是程式語言』的願景已經不再是夢想。終端用戶將使用自然語言操控應用程式, 由語言模型自主理解規劃邏輯流程, 我們的程式則是語言模型背後的軍火庫, 串接外部各式各樣的功能。| 用真實上線的 GPTs 學 action 開發 |即使 GPTs 已經提供搜尋網路、上傳檔案的功能, 仍然無法隨意取用外部功能, 因此 GPTs 最關鍵的環節就是 action 外掛系統, 讓語言模型與外界接軌。本書會帶大家用真實上線的台鐵時刻表查詢專家當範例, 手把手教你設計 GPTs action, 補強客製 GPTs 欠缺的功能。| 串接 Google OAuth2 API 管控 action 使用者 |客製的 GPTs 要能針對不同使用者提供客製服務才是真客製, 本書會說明如何串接 Google OAuth2 API, 幫 GPTs action 加上登入 Google 帳號機制, 以便後端程式可以根據登入的使用者提供不同的服務, 設計出真正客製的 ChatGPT。| Chat/Assistants API 全解析 |Assistants API 是 OpenAI API 家族的大禮, 以往我們必須自己處理的對話記錄、網路搜尋、向量資料庫、程式執行工具、文字生圖等等功能, 現在全部整合在 Assistants API 中, 成為實作 AI 助理的基石。本書會帶大家深度瞭解 Chat API 與 Assistants API 的差異, 讓你針對問題挑選適當的 API。| 深入 function calling 機制 |即使是最新的 gpt-4 模型, 訓練資料也僅止於 2023 年 4 月, 模型本身也不能連網取得新知, 更無法介接外部 API。透過 function calling 機制, 就可以撰寫各式各樣的函式作為延伸語言模型能力的外掛, 任由模型取用。新版模型更強化功能, 提供 parallel function calling, 即使複雜問題也能自動分解由多個函式完成。| 整合 gpt-4-vision 模型 |gpt-4-vision 讓模型長眼睛, 本書就利用這項功

「一本平易近人的著作，可讓你立即應用進而提升程式的執行效率。書中將教你電腦科學中會用到的基本演算法和資料型別。如果你正在找一份程式設計相關的技術工作，這本書可能會在下次的程式面試中幫助你取得好成績。」 ― Zvi Galil 喬治亞理工學院 計算學院Frederick G. Storey主任暨名譽院長 論及撰寫有效率的程式時，每位軟體專業人士都需要具備有效率的演算法運作知識。在這本實務書籍中，《Algorithms in a Nutshell》作者George Heineman對於多種語言編寫程式時所用的效能改進關鍵演算法，有簡潔而詳實的介紹。軟體開發人員、測試人員、維護人員將理解演算法如何創造性地解決運算問題。 每章都會以前面章節內容為基礎，藉由清晰的圖示和不斷提供的新基本概念，包括演算法分析，對書中介紹的每個演算法做效能分級。你可以將每章所學到的內容，應用到該章最後的挑戰題中，如同模擬在程式術科面試場上的體驗。 閱讀本書，你將可以： ‧探索電腦科學和軟體工程核心的基本演算法 ‧學習有效率解決問題的常見策略，例如：分治法、動態規劃、貪婪方法 ‧使用Big-O分析與評估程式的時間複雜度 ‧使用現有的Python函式庫和資料結構來解決演算法問題 ‧了解重要演算法的主要步驟

This book describes the methods and algorithms for image pre-processing and recognition. These methods are based on a parallel shift technology of the imaging copy, as well as simple mathematical oper

The constantly increasing demand for more computing power can seem impossible to keep up with. However, multicore processors capable of performing computations in parallel allow computers to tackle ever larger problems in a wide variety of applications. This book provides a comprehensive introduction to parallel computing, discussing theoretical issues such as the fundamentals of concurrent processes, models of parallel and distributed computing, and metrics for evaluating and comparing parallel algorithms, as well as practical issues, including methods of designing and implementing shared- and distributed-memory programs, and standards for parallel program implementation, in particular MPI and OpenMP interfaces. Each chapter presents the basics in one place followed by advanced topics, allowing novices and experienced practitioners to quickly find what they need. A glossary and more than 80 exercises with selected solutions aid comprehension. The book is recommended as a text for adva

"This book provides insight on the latest advances and analysis of technologies in metaheuristics computing, offering widespread coverage on topics such as genetic algorithms, differential evolution,

Most textbooks on modern heuristics provide the reader with detailed descriptions of the functionality of single examples like genetic algorithms, genetic programming, tabu search, simulated annealing

Randomized search heuristics such as evolutionary algorithms, genetic algorithms, evolution strategies, ant colony and particle swarm optimization turn out to be highly successful for optimization in

Various problems in computer science are 'hard', that is NP-complete, and so not realistically computable; thus in order to solve them they have to be approximated. This book is a survey of the basic techniques for approximating combinatorial problems using parallel algorithms. Its core is a collection of techniques that can be used to provide parallel approximations for a wide range of problems (for example, flows, coverings, matchings, travelling salesman problems, graphs), but in order to make the book reasonably self-contained, the authors provide an introductory chapter containing the basic definitions and results. A final chapter deals with problems that cannot be approximated, and the book is ended by an appendix that gives a convenient summary of the problems described in the book. This is an up-to-date reference for research workers in the area of algorithms, but it can also be used for graduate courses in the subject.

Evolutionary computation, a broad field that includes genetic algorithms, evolution strategies, and evolutionary programming, has proven to offer well-suited techniques for industrial and management t

Various problems in computer science are 'hard', that is NP-complete, and so not realistically computable; thus in order to solve them they have to be approximated. This book is a survey of the basic techniques for approximating combinatorial problems using parallel algorithms. Its core is a collection of techniques that can be used to provide parallel approximations for a wide range of problems (for example, flows, coverings, matchings, travelling salesman problems, graphs), but in order to make the book reasonably self-contained, the authors provide an introductory chapter containing the basic definitions and results. A final chapter deals with problems that cannot be approximated, and the book is ended by an appendix that gives a convenient summary of the problems described in the book. This is an up-to-date reference for research workers in the area of algorithms, but it can also be used for graduate courses in the subject.

The field of human genetics has witnessed tremendous acceleration in the last decade. The parallel advances in genotyping and sequencing technology, our understanding of human genetic variation, and t

This book focuses on architecture and implementation of algorithms, specifically on their real-time and causal processing implementation, architectures of FPGA design and parallel processing. It conc

Efficient Learning Machines explores all the major topics of machine learning, including big data, knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and gami

Domain decomposition (DD) methods provide powerful tools for constructing parallel numerical solution algorithms for large scale systems of algebraic equations arising from the discretization of parti

Single and Multi-Objective Evolutionary Computation (MOEA), Genetic Algorithms (GAs), Artificial Neural Networks (ANNs), Fuzzy Controllers (FCs), Particle Swarm Optimization (PSO) and Ant colony Optim

This book covers up-to-date methods and algorithms for the automated analysis of engineering drawings and digital cartographic maps. The Non-Deterministic Agent System (NDAS) offers a parallel computa

This work fills the gap for a comprehensive reference conveying the developments in global optimization of atomic structures using genetic algorithms. Over the last few decades, such algorithms based

Soft Computing is a complex of methodologies that includes artificial neural networks, genetic algorithms, fuzzy logic, Bayesian networks, and their hybrids. It admits approximate reasoning, imprecisi

Solving complex optimization problems with parallel metaheuristics Parallel Metaheuristics brings together an international group of experts in parallelism and metaheuristics to provide a much-needed

This book describes a fundamentally new theoretical framework for finding poor algorithms in an application program and replacing them with ones that parallelize the code. Parallel computation will

This book covers the techniques of data mining, knowledge discovery, genetic algorithms, neural networks, bootstrapping, machine learning, and Monte Carlo simulation. Computational finance, an excit

1.Covers not only AI theory, but modern applications e.g., game programming, machine learning, swarming, artificial immune systems, genetic algorithms, pattern recognition, numerical optimization, dat

The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.

The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.

The bestselling author of The Accidental Billionaires and The 37th Parallel tells the fascinating Jurassic Park­-like story of the genetic restoration of an extinct species—the woolly mammo

Soft computing methods such as neural networks and genetic algorithms draw on the problem solving strategies of the natural world which differ fundamentally from the mathematically-based computing met

"This book explores the various methods for inventory control and management using optimization techniques, data mining concepts and genetic algorithms. It explores how inventory control and managemen

The bestselling author of The Accidental Billionaires and The 37th Parallel tells the fascinating Jurassic Park­-like story of the genetic restoration of an extinct species—the woolly mammoth. “Pa

OpenACC Programming: Simplifying Massively Parallel Computing is a modern, practical guide to implementing dependable computing systems. OpenACC Programming: Simplifying Massively Parallel Computing e

This book is primarily intended as a research monograph that could also be used in graduate courses for the design of parallel algorithms in matrix computations.It assumes general but not extensive kn

This book presents a new computational finance approach combining a Symbolic Aggregate approximation (SAX) technique with an optimization kernel based on genetic algorithms (GA). While the SAX represe

There is a software gap between the hardware potential and the performance that can be attained using today's software parallel program development tools. The tools need manual intervention by the pro

Visitors to the velvet underground of computing are apt to encounter fuzzy sets, neural networks, genetic algorithms, Bayesian belief networks, rough sets, and other methodologies of uncertainty. Comp

In this book, we aim to collect the most recent advances in artificial intelligence techniques (i.e. neural networks, fuzzy systems, multi-agent systems, genetic algorithms, image analysis, clustering

New perspective technologies of genetic search and evolution simulation represent the kernel of this book. The authors wanted to show how these technologies are used for practical problems solution. T

The ability of parallel computing to process large data sets and handle time-consuming operations has resulted in unprecedented advances in biological and scientific computing, modeling, and simulatio

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, ca