Essentials of Metaheuristics
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Optimisation Technical Background
Sign in Sign up. Launching GitHub Desktop Go back. Launching Xcode Launching Visual Studio Please tell me of any errors you find and correct! Some complex algorithms have been presented in simplified versions. What is a Metaheuristic? A common but unfortunate name for any stochastic optimization algorithm intended to be the last resort before giving up and using random or brute-force search.
Such algorithms are used for problems where you don't know how to find a good solution, but if shown a candidate solution, you can give it a grade. The algorithmic family includes genetic algorithms, hill-climbing, simulated annealing, ant colony optimization, particle swarm optimization, and so on. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.
L'apprentissage automatique est un domaine aux multiples facettes. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization ACO , the most successful and widely recognized algorithmic technique based on ant behavior.
This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms.
The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings.
The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises.
Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms. From Real to Artificial Ants Chapter 2. Ant Colony Optimization Theory Chapter 5.
Conclusions and Prospects for the Future. Les autres chapitres sont essentiellement des chapitres d'approfondissement. Langdon et Nicholas Freitag McPhee.
Chapter 6. Optimization Algorithms
Genetic programming GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until high-fitness solutions emerge.
All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions.
Sean Luke: Essentials of Metaheuristics
This unique overview of this exciting technique is written by three of the most active scientists in GP. Introduction Part I: Basics Chapter 2. Linear and Graph Genetic Programming Chapter 8. Probabilistic Genetic Programming Chapter 9. Multi-objective Genetic Programming Chapter Fast and Distributed Genetic Programming Chapter Applications Chapter Troubleshooting GP Chapter Resources Appendix B.
L'avez-vous lu? Quel est votre avis? Essentials of Metaheuristics de Sean Luke. Interested in the Genetic Algorithm? Simulated Annealing?
How It Works
Ant Colony Optimization? Essentials of Metaheuristics covers these and other metaheuristics algorithms, and is intended for undergraduate students, programmers, and non-experts. The book covers a wide range of algorithms, representations, selection and modification operators, and related topics, and includes 70 figures and algorithms great and small. Chapter 0. Introduction Chapter 1. Gradient-based Optimization Chapter 2. Single-State Methods Chapter 3.
Population Methods Chapter 4. Representation Chapter 5. Parallel Methods Chapter 6. Coevolution Chapter 7.