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.
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First Sentence:
The first steps towards the development of competent genetic algorithms were taken by Goldberg, Korb, and Deb (1989) who developed the messy GA in 1989. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
absolute ordering problem, correct subfunctions, subfunction genes, splice probability, total transition time, juxtapositional phase, deceptive problems, maximum era, competent genetic algorithms, splice operators, messy genetic algorithm, char msg, deceptive functions, average makespan, cut probability, nonuniform scaling, filtering phase, int low, absolute problem, permutation problems, insufficient memory, random keys, exponential scaling, chromosome part, problem length New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The first steps towards the development of competent genetic algorithms were taken by Goldberg, Korb, and Deb (1989) who developed the messy GA in 1989. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
absolute ordering problem, correct subfunctions, subfunction genes, splice probability, total transition time, juxtapositional phase, deceptive problems, maximum era, competent genetic algorithms, splice operators, messy genetic algorithm, char msg, deceptive functions, average makespan, cut probability, nonuniform scaling, filtering phase, int low, absolute problem, permutation problems, insufficient memory, random keys, exponential scaling, chromosome part, problem length New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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This book cites 8
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8
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- Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic Algorithms and Evolutionary Computation) by Carlos A. Coello Coello in Front Matter
- Artificial Intelligence by A. E. Nisenfedl in Back Matter
- Anticipatory Learning Classifier Systems (Genetic Algorithms and Evolutionary Computation) by Martin V. Butz in Front Matter
- Simulated Annealing (Sa and Optimization : Modern Algorithms With Vlsi, Optimal Design, and Missile Defense Applications) by Mark E. Johnson in Back Matter
- Evolutionary Optimization in Dynamic Environments (Genetic Algorithms and Evolutionary Computation) by Jürgen Branke in Front Matter
See all 8 books this book cites
- Credit Ratings: Methodologies, Rationale and Default Risk by Michael K. Ong on page 212
- Evolutionary Optimization in Dynamic Environments (Genetic Algorithms and Evolutionary Computation) by Jürgen Branke in Front Matter
- Anticipatory Learning Classifier Systems (Genetic Algorithms and Evolutionary Computation) by Martin V. Butz in Front Matter
- Frontiers of Evolutionary Computation (Genetic Algorithms and Evolutionary Computation) by Anil Menon in Front Matter
- Noisy Optimization with Evolution Strategies (Genetic Algorithms and Evolutionary Computation) by Dirk V. Arnold in Front Matter
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