Genetic Algorithms in Java Basics, Lee Jacobson; Burak Kanber
Автор: Cormen, Thomas H., E Название: Introduction to algorithms 3 ed. ISBN: 0262033844 ISBN-13(EAN): 9780262033848 Издательство: MIT Press Рейтинг: Цена: 27588.00 р. Наличие на складе: Нет в наличии.
Описание: A new edition of the essential text and professional reference, with substantial new material on such topics as vEB trees, multithreaded algorithms, dynamic programming, and edge-base flow.
Автор: Coley, David Название: Introduction to genetic algorithms for scientists and engineers ISBN: 9810236026 ISBN-13(EAN): 9789810236021 Издательство: World Scientific Publishing Рейтинг: Цена: 6494.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The approach taken in this text is largely practical, with algorithms being presented in full and working code (in BASIC, FORTRAN, PASCAL and C) on the accompanying disk. Exercises are included at the end of several chapters, many of which are computer based.
Автор: Viktor M. Kureichik; Sergey P. Malioukov; Vladimir Название: Genetic Algorithms for Applied CAD Problems ISBN: 3540852808 ISBN-13(EAN): 9783540852803 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: With new perspective technologies of genetic search and evolution simulation at the heart of this book, the authors wanted to show how these technologies are used for the solution of practical problems. This monograph is devoted to specialists of CAD.
Автор: Franz Rothlauf Название: Representations for Genetic and Evolutionary Algorithms ISBN: 3642064108 ISBN-13(EAN): 9783642064104 Издательство: Springer Рейтинг: Цена: 23508.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has been focused on operators and test problems, while problem representation has often been taken as given.
Автор: Zbigniew Michalewicz Название: Genetic Algorithms + Data Structures = Evolution Programs ISBN: 3642082335 ISBN-13(EAN): 9783642082337 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control.
Автор: Affenzeller, Michael (upper Austria University Of Applied Sciences, Hagenberg, And Johannes Kepler University Of Linz, Austria) Wagner, Stefan (upper Название: Genetic algorithms and genetic programming ISBN: 1138114278 ISBN-13(EAN): 9781138114272 Издательство: Taylor&Francis Рейтинг: Цена: 11176.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications discusses algorithmic developments in the context of genetic algorithms (GAs) and genetic programming (GP). It applies the algorithms to significant combinatorial optimization problems and describes structure identification using HeuristicLab as a platform for algorithm development.
The book focuses on both theoretical and empirical aspects. The theoretical sections explore the important and characteristic properties of the basic GA as well as main characteristics of the selected algorithmic extensions developed by the authors. In the empirical parts of the text, the authors apply GAs to two combinatorial optimization problems: the traveling salesman and capacitated vehicle routing problems. To highlight the properties of the algorithmic measures in the field of GP, they analyze GP-based nonlinear structure identification applied to time series and classification problems.
Written by core members of the HeuristicLab team, this book provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts. By comparing the results of standard GA and GP implementation with several algorithmic extensions, it also shows how to substantially increase achievable solution quality.
Self-driving cars, natural language recognition, and online recommendation engines are all possible thanks to Machine Learning. Now you can create your own genetic algorithms, nature-inspired swarms, Monte Carlo simulations, cellular automata, and clusters. Learn how to test your ML code and dive into even more advanced topics. If you are a beginner-to-intermediate programmer keen to understand machine learning, this book is for you.
Discover machine learning algorithms using a handful of self-contained recipes. Build a repertoire of algorithms, discovering terms and approaches that apply generally. Bake intelligence into your algorithms, guiding them to discover good solutions to problems.
In this book, you will:
Use heuristics and design fitness functions.
Build genetic algorithms.
Make nature-inspired swarms with ants, bees and particles.
Create Monte Carlo simulations.
Investigate cellular automata.
Find minima and maxima, using hill climbing and simulated annealing.
Try selection methods, including tournament and roulette wheels.
Learn about heuristics, fitness functions, metrics, and clusters.
Test your code and get inspired to try new problems. Work through scenarios to code your way out of a paper bag; an important skill for any competent programmer. See how the algorithms explore and learn by creating visualizations of each problem. Get inspired to design your own machine learning projects and become familiar with the jargon.
What You Need:
Code in C++ (>= C++11), Python (2.x or 3.x) and JavaScript (using the HTML5 canvas). Also uses matplotlib and some open source libraries, including SFML, Catch and Cosmic-Ray. These plotting and testing libraries are not required but their use will give you a fuller experience. Armed with just a text editor and compiler/interpreter for your language of choice you can still code along from the general algorithm descriptions.
Описание: If you`re a student studying computer science or a software developer preparing for technical interviews, this practical book will help you learn and review some of the most important ideas in software engineering-data structures and algorithms-in a way that`s clearer, more concise, and more engaging than other materials.
Описание: Genetic Algorithms (GA) and Artificial Neural Networks (ANN) have progressively increased in importance amongst the techniques routinely used in chemometrics. Divided into two sections (GA and ANN), this book contains contributions from experts in the field and is of use to those who are using or are interested in GA and ANN.
Автор: Mutingi Название: Grouping Genetic Algorithms ISBN: 3319443933 ISBN-13(EAN): 9783319443935 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision criteria. Examples of such problems are: vehicle routing problems, team formation problems, timetabling problems, assembly line balancing, group maintenance planning, modular design, and task assignment. A wide range of industrial grouping problems, drawn from diverse fields such as logistics, supply chain management, project management, manufacturing systems, engineering design and healthcare, are presented. Typical complex industrial grouping problems, with multiple decision criteria and constraints, are clearly described using illustrative diagrams and formulations. The problems are mapped into a common group structure that can conveniently be used as an input scheme to specific variants of grouping genetic algorithms. Unique heuristic grouping techniques are developed to handle grouping problems efficiently and effectively. Illustrative examples and computational results are presented in tables and graphs to demonstrate the efficiency and effectiveness of the algorithms.Researchers, decision analysts, software developers, and graduate students from various disciplines will find this in-depth reader-friendly exposition of advances and applications of grouping genetic algorithms an interesting, informative and valuable resource.
Автор: Roughgarden Tim Название: Algorithms Illuminated, Part 1: The Basics ISBN: 0999282905 ISBN-13(EAN): 9780999282908 Издательство: Неизвестно Цена: 2757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides a conceptual approach that will encourage students learning algebra to employ a variety of thinking processes and strategies and, most importantly, will enable them to truly understand the concepts that underlie the problems they are solving. The book includes tasks focusing on algebraic expressions, equations, and functions, followed by tasks that integrate several mathematical concepts.
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