Heuristics For Optimization And Learning
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| Author | : Farouk Yalaoui |
| Publisher | : Springer Nature |
| Total Pages | : 444 |
| Release | : 2020-12-15 |
| Genre | : Technology & Engineering |
| ISBN | : 3030589307 |
This book is a new contribution aiming to give some last research findings in the field of optimization and computing. This work is in the same field target than our two previous books published: “Recent Developments in Metaheuristics” and “Metaheuristics for Production Systems”, books in Springer Series in Operations Research/Computer Science Interfaces. The challenge with this work is to gather the main contribution in three fields, optimization technique for production decision, general development for optimization and computing method and wider spread applications. The number of researches dealing with decision maker tool and optimization method grows very quickly these last years and in a large number of fields. We may be able to read nice and worthy works from research developed in chemical, mechanical, computing, automotive and many other fields.
| Author | : Kwang Y. Lee |
| Publisher | : John Wiley & Sons |
| Total Pages | : 616 |
| Release | : 2008-01-28 |
| Genre | : Technology & Engineering |
| ISBN | : 0470225858 |
This book explores how developing solutions with heuristic tools offers two major advantages: shortened development time and more robust systems. It begins with an overview of modern heuristic techniques and goes on to cover specific applications of heuristic approaches to power system problems, such as security assessment, optimal power flow, power system scheduling and operational planning, power generation expansion planning, reactive power planning, transmission and distribution planning, network reconfiguration, power system control, and hybrid systems of heuristic methods.
| Author | : Anuj Kumar |
| Publisher | : Walter de Gruyter GmbH & Co KG |
| Total Pages | : 219 |
| Release | : 2022-01-19 |
| Genre | : Computers |
| ISBN | : 3110716259 |
This book offers a thorough overview of the most popular and researched meta-heuristic optimization techniques and nature-inspired algorithms. Their wide applicability makes them a hot research topic and an effi cient tool for the solution of complex optimization problems in various fi elds of sciences, engineering, and in numerous industries.
| Author | : Bernabé Dorronsoro |
| Publisher | : Springer Nature |
| Total Pages | : 230 |
| Release | : 2022-02-21 |
| Genre | : Computers |
| ISBN | : 3030942163 |
This volume constitutes selected papers presented during the 8th International Conference on Metaheuristics and Nature Inspired Computing, META 2021, held in Marrakech, Morocco, in October 201. Due to the COVID-19 pandemic the conference was partiqally held online. The 16 papers were thoroughly reviewed and selected from the 53 submissions. They are organized in the topical sections on combinatorial optimization; continuous optimization; optimization and machine learning; applications.
| Author | : Mauricio G.C. Resende |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 744 |
| Release | : 2003-11-30 |
| Genre | : Computers |
| ISBN | : 9781402076534 |
Combinatorial optimization is the process of finding the best, or optimal, so lution for problems with a discrete set of feasible solutions. Applications arise in numerous settings involving operations management and logistics, such as routing, scheduling, packing, inventory and production management, lo cation, logic, and assignment of resources. The economic impact of combi natorial optimization is profound, affecting sectors as diverse as transporta tion (airlines, trucking, rail, and shipping), forestry, manufacturing, logistics, aerospace, energy (electrical power, petroleum, and natural gas), telecommu nications, biotechnology, financial services, and agriculture. While much progress has been made in finding exact (provably optimal) so lutions to some combinatorial optimization problems, using techniques such as dynamic programming, cutting planes, and branch and cut methods, many hard combinatorial problems are still not solved exactly and require good heuristic methods. Moreover, reaching "optimal solutions" is in many cases meaningless, as in practice we are often dealing with models that are rough simplifications of reality. The aim of heuristic methods for combinatorial op timization is to quickly produce good-quality solutions, without necessarily providing any guarantee of solution quality. Metaheuristics are high level procedures that coordinate simple heuristics, such as local search, to find solu tions that are of better quality than those found by the simple heuristics alone: Modem metaheuristics include simulated annealing, genetic algorithms, tabu search, GRASP, scatter search, ant colony optimization, variable neighborhood search, and their hybrids.
| Author | : Yoshua Bengio |
| Publisher | : Now Publishers Inc |
| Total Pages | : 145 |
| Release | : 2009 |
| Genre | : Computational learning theory |
| ISBN | : 1601982941 |
Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
| Author | : El-Ghazali Talbi |
| Publisher | : John Wiley & Sons |
| Total Pages | : 625 |
| Release | : 2009-05-27 |
| Genre | : Computers |
| ISBN | : 0470496908 |
A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to implement the algorithms under a software framework to reuse both the design and code. Throughout the book, the key search components of metaheuristics are considered as a toolbox for: Designing efficient metaheuristics (e.g. local search, tabu search, simulated annealing, evolutionary algorithms, particle swarm optimization, scatter search, ant colonies, bee colonies, artificial immune systems) for optimization problems Designing efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing metaheuristics on sequential and parallel machines Using many case studies and treating design and implementation independently, this book gives readers the skills necessary to solve large-scale optimization problems quickly and efficiently. It is a valuable reference for practicing engineers and researchers from diverse areas dealing with optimization or machine learning; and graduate students in computer science, operations research, control, engineering, business and management, and applied mathematics.
| Author | : Diego Oliva |
| Publisher | : Springer Nature |
| Total Pages | : 765 |
| Release | : |
| Genre | : Computational intelligence |
| ISBN | : 3030705420 |
This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.
| Author | : Franz Rothlauf |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 268 |
| Release | : 2011-07-17 |
| Genre | : Computers |
| ISBN | : 3540729623 |
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, and others, but fail to teach the underlying concepts behind these different approaches. The author takes a different approach in this textbook by focusing on the users' needs and answering three fundamental questions: First, he tells us which problems modern heuristics are expected to perform well on, and which should be left to traditional optimization methods. Second, he teaches us to systematically design the "right" modern heuristic for a particular problem by providing a coherent view on design elements and working principles. Third, he shows how we can make use of problem-specific knowledge for the design of efficient and effective modern heuristics that solve not only small toy problems but also perform well on large real-world problems. This book is written in an easy-to-read style and it is aimed at students and practitioners in computer science, operations research and information systems who want to understand modern heuristics and are interested in a guide to their systematic design and use. This book is written in an easy-to-read style and it is aimed at students and practitioners in computer science, operations research and information systems who want to understand modern heuristics and are interested in a guide to their systematic design and use. This book is written in an easy-to-read style and it is aimed at students and practitioners in computer science, operations research and information systems who want to understand modern heuristics and are interested in a guide to their systematic design and use.
| Author | : Bernadete Ribeiro |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 561 |
| Release | : 2005-12-12 |
| Genre | : Computers |
| ISBN | : 3211273891 |
The ICANNGA series of Conferences has been organised since 1993 and has a long history of promoting the principles and understanding of computational intelligence paradigms within the scientific community and is a reference for established workers in this area. Starting in Innsbruck, in Austria (1993), then to Ales in Prance (1995), Norwich in England (1997), Portoroz in Slovenia (1999), Prague in the Czech Republic (2001) and finally Roanne, in France (2003), the ICANNGA series has established itself for experienced workers in the field. The series has also been of value to young researchers wishing both to extend their knowledge and experience and also to meet internationally renowned experts. The 2005 Conference, the seventh in the ICANNGA series, will take place at the University of Coimbra in Portugal, drawing on the experience of previous events, and following the same general model, combining technical sessions, including plenary lectures by renowned scientists, with tutorials.
