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eBook OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems (Genetic Algorithms and Evolutionary Computation) ePub

eBook OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems (Genetic Algorithms and Evolutionary Computation) ePub

by Dimitri Knjazew

  • ISBN: 0792374606
  • Category: Computer Science
  • Subcategory: Computers
  • Author: Dimitri Knjazew
  • Language: English
  • Publisher: Springer; 2002 edition (January 31, 2002)
  • Pages: 152
  • ePub book: 1576 kb
  • Fb2 book: 1649 kb
  • Other: azw lrf azw docx
  • Rating: 4.7
  • Votes: 645

Description

Series: Genetic Algorithms and Evolutionary Computation (Book 6). Hardcover: 152 pages. Start reading OmeGA on your Kindle in under a minute.

Finally, the book applies the algorithm to a test function drawn from the literature of scheduling.

OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems addresses two increasingly important areas in GA implementation and practice. Permutation and scheduling problems are difficult combinatorial optimization problems with commercial import across a variety of industries. This book approaches both subjects systematically and clearly. Finally, the book applies the algorithm to a test function drawn from the literature of scheduling.

Электронная книга "OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems", Dimitri Knjazew. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст,. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems" для чтения в офлайн-режиме.

oceedings{Knjazew2002OmeGAA, title {OmeGA - a competent genetic algorithm for solving .

oceedings{Knjazew2002OmeGAA, title {OmeGA - a competent genetic algorithm for solving permutation and scheduling problems}, author {Dimitri Knjazew}, booktitle {Genetic algorithms and evolutionary computation}, year {2002} }. Dimitri Knjazew. Published in. Genetic algorithms an. 002.

Competent GAs are those designed for principled solutions OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems addresses two increasingly important areas in GA implementation and practice.

Автор: Knjazew Dimitri Название: OmeGA, A Competent Genetic Algorithm for Solving .

A competent genetic algorithm for solving permutation and scheduling problems.

Competent GAs are those designed for principled solutions of hard problems, quickly, reliably, and accurately.

Genetic Algorithms use a direct analogy of natural behavior. They work with a population of individuals, each representing a possible solution to a given problem. Each individual is assigned a fitness score according to how good a solution to the problem it is. The highly fit individuals are given opportunities to reproduce, by cross breeding with other individuals in the population.

Genetic algorithms (GAs), subfield of evolutionary computation, were .

Genetic algorithms (GAs), subfield of evolutionary computation, were invented and developed during the 60s at the University of Michigan by John Holland and its students12. The field of evolutionary computation is considered a strong tool to deal with complex problem solving. Also, as the human organism develops complex mechanisms to solve problems that would threaten his survival, computer programs need to develop innovative ways to get an optimal solution. The constant changes in the computational world are well represented by the ever-changing natural world.

OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems addresses two increasingly important areas in GA implementation and practice. OmeGA, or the ordering messy genetic algorithm, combines some of the latest in competent GA technology to solve scheduling and other permutation problems. Competent GAs are those designed for principled solutions of hard problems, quickly, reliably, and accurately. Permutation and scheduling problems are difficult combinatorial optimization problems with commercial import across a variety of industries.

This book approaches both subjects systematically and clearly. The first part of the book presents the clearest description of messy GAs written to date along with an innovative adaptation of the method to ordering problems. The second part of the book investigates the algorithm on boundedly difficult test functions, showing principled scale up as problems become harder and longer. Finally, the book applies the algorithm to a test function drawn from the literature of scheduling.