Download or read book The Problem of Tuning Metaheuristics written by Mauro Birattari and published by . This book was released on 2005 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The Problem of Tuning Metaheuristics as Seen from a Machine Learning Perspective written by Mauro Birattari and published by IOS Press. This book was released on 2005 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A metaheuristic is a generic algorithmic template that can be used for finding high quality solutions of optimization problems. The authors show that the problem of tuning a metaheuristic can be described and solved as a machine learning problem. This thesis contains an experimental analysis of F-Race and some examples of practical applications."
Download or read book Tuning Metaheuristics written by Mauro Birattari and published by Springer Science & Business Media. This book was released on 2009-04-08 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book lays the foundations for a scientific approach to tuning metaheuristics. The fundamental intuition that underlies Birattari's approach is that the tuning problem has much in common with the problems that are typically faced in machine learning.
Download or read book Metaheuristics written by Karl F. Doerner and published by Springer Science & Business Media. This book was released on 2007-08-13 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book’s aim is to provide several different kinds of information: a delineation of general metaheuristics methods, a number of state-of-the-art articles from a variety of well-known classical application areas as well as an outlook to modern computational methods in promising new areas. Therefore, this book may equally serve as a textbook in graduate courses for students, as a reference book for people interested in engineering or social sciences, and as a collection of new and promising avenues for researchers working in this field.
Download or read book Metaheuristics for Machine Learning written by Kanak Kalita and published by John Wiley & Sons. This book was released on 2024-03-28 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: METAHEURISTICS for MACHINE LEARNING The book unlocks the power of nature-inspired optimization in machine learning and presents a comprehensive guide to cutting-edge algorithms, interdisciplinary insights, and real-world applications. The field of metaheuristic optimization algorithms is experiencing rapid growth, both in academic research and industrial applications. These nature-inspired algorithms, which draw on phenomena like evolution, swarm behavior, and neural systems, have shown remarkable efficiency in solving complex optimization problems. With advancements in machine learning and artificial intelligence, the application of metaheuristic optimization techniques has expanded, demonstrating significant potential in optimizing machine learning models, hyperparameter tuning, and feature selection, among other use-cases. In the industrial landscape, these techniques are becoming indispensable for solving real-world problems in sectors ranging from healthcare to cybersecurity and sustainability. Businesses are incorporating metaheuristic optimization into machine learning workflows to improve decision-making, automate processes, and enhance system performance. As the boundaries of what is computationally possible continue to expand, the integration of metaheuristic optimization and machine learning represents a pioneering frontier in computational intelligence, making this book a timely resource for anyone involved in this interdisciplinary field. Metaheuristics for Machine Learning: Algorithms and Applications serves as a comprehensive guide to the intersection of nature-inspired optimization and machine learning. Authored by leading experts, this book seamlessly integrates insights from computer science, biology, and mathematics to offer a panoramic view of the latest advancements in metaheuristic algorithms. You’ll find detailed yet accessible discussions of algorithmic theory alongside real-world case studies that demonstrate their practical applications in machine learning optimization. Perfect for researchers, practitioners, and students, this book provides cutting-edge content with a focus on applicability and interdisciplinary knowledge. Whether you aim to optimize complex systems, delve into neural networks, or enhance predictive modeling, this book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence. Audience The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms.
Download or read book Intelligent Computational Optimization in Engineering written by Mario Köppen and published by Springer. This book was released on 2011-07-15 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: We often come across computational optimization virtually in all branches of engineering and industry. Many engineering problems involve heuristic search and optimization, and, once discretized, may become combinatorial in nature, which gives rise to certain difficulties in terms of solution procedure. Some of these problems have enormous search spaces, are NP-hard and hence require heuristic solution techniques. Another difficulty is the lack of ability of classical solution techniques to determine appropriate optima of non-convex problems. Under these conditions, recent advances in computational optimization techniques have been shown to be advantageous and successful compared to classical approaches. This Volume presents some of the latest developments with a focus on the design of algorithms for computational optimization and their applications in practice. Through the chapters of this book, researchers and practitioners share their experience and newest methodologies with regard to intelligent optimization and provide various case studies of the application of intelligent optimization techniques in real-world applications.This book can serve as an excellent reference for researchers and graduate students in computer science, various engineering disciplines and the industry.
Download or read book Automated Design of Machine Learning and Search Algorithms written by Nelishia Pillay and published by Springer Nature. This book was released on 2021-07-28 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future directions in this fast-developing area. Methods have been developed to automate the design of neural networks, heuristics and metaheuristics using techniques such as metaheuristics, statistical techniques, machine learning and hyper-heuristics. The book first defines the field of automated design, distinguishing it from the similar but different topics of automated algorithm configuration and automated algorithm selection. The chapters report on the current state of the art by experts in the field and include reviews of AutoML and automated design of search, theoretical analyses of automated algorithm design, automated design of control software for robot swarms, and overfitting as a benchmark and design tool. Also covered are automated generation of constructive and perturbative low-level heuristics, selection hyper-heuristics for automated design, automated design of deep-learning approaches using hyper-heuristics, genetic programming hyper-heuristics with transfer knowledge and automated design of classification algorithms. The book concludes by examining future research directions of this rapidly evolving field. The information presented here will especially interest researchers and practitioners in the fields of artificial intelligence, computational intelligence, evolutionary computation and optimisation.
Download or read book Hybrid Metaheuristics written by Thomas Bartz-Beielstein and published by Springer. This book was released on 2007-09-20 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Workshop on Hybrid Metaheuristics, HM 2007, held in Dortmund, Germany. The 14 revised full papers discuss specific aspects of hybridization of metaheuristics, hybrid metaheuristics design, development and testing. With increasing attention to methodological aspects, from both the empirical and theoretical sides, the papers show a representative sample of research in the field of hybrid metaheuristics.
Download or read book Handbook of Research on Metaheuristics for Order Picking Optimization in Warehouses to Smart Cities written by Ochoa Ortiz-Zezzatti, Alberto and published by IGI Global. This book was released on 2019-04-05 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building accurate algorithms for the optimization of picking orders is a difficult task, especially when one considers the delays of real-world situations. In warehouse environments, diverse algorithms must be developed to enhance the global performance relating to combining customer orders into picking orders to reduce wait times. The Handbook of Research on Metaheuristics for Order Picking Optimization in Warehouses to Smart Cities is a pivotal reference source that addresses strategies for developing able algorithms in order to build better picking orders and the impact of these strategies on the picking systems in which diverse algorithms are implemented. While highlighting topics such ABC optimization, environmental intelligence, and order batching, this publication examines common picking aspects in warehouse environments ranging from manual order picking systems to automated retrieval systems. This book is intended for researchers, teachers, engineers, managers, and practitioners seeking research on algorithms to enhance the order picking performance.
Download or read book Experimental Methods for the Analysis of Optimization Algorithms written by Thomas Bartz-Beielstein and published by Springer Science & Business Media. This book was released on 2010-11-02 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability and reproducibility of results. However, computational experiments differ from those in other sciences, and the last decade has seen considerable methodological research devoted to understanding the particular features of such experiments and assessing the related statistical methods. This book consists of methodological contributions on different scenarios of experimental analysis. The first part overviews the main issues in the experimental analysis of algorithms, and discusses the experimental cycle of algorithm development; the second part treats the characterization by means of statistical distributions of algorithm performance in terms of solution quality, runtime and other measures; and the third part collects advanced methods from experimental design for configuring and tuning algorithms on a specific class of instances with the goal of using the least amount of experimentation. The contributor list includes leading scientists in algorithm design, statistical design, optimization and heuristics, and most chapters provide theoretical background and are enriched with case studies. This book is written for researchers and practitioners in operations research and computer science who wish to improve the experimental assessment of optimization algorithms and, consequently, their design.
Download or read book Handbook of Metaheuristics written by Michel Gendreau and published by Springer. This book was released on 2018-09-20 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: The third edition of this handbook is designed to provide a broad coverage of the concepts, implementations, and applications in metaheuristics. The book’s chapters serve as stand-alone presentations giving both the necessary underpinnings as well as practical guides for implementation. The nature of metaheuristics invites an analyst to modify basic methods in response to problem characteristics, past experiences, and personal preferences, and the chapters in this handbook are designed to facilitate this process as well. This new edition has been fully revised and features new chapters on swarm intelligence and automated design of metaheuristics from flexible algorithm frameworks. The authors who have contributed to this volume represent leading figures from the metaheuristic community and are responsible for pioneering contributions to the fields they write about. Their collective work has significantly enriched the field of optimization in general and combinatorial optimization in particular.Metaheuristics are solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of a solution space. In addition, many new and exciting developments and extensions have been observed in the last few years. Hybrids of metaheuristics with other optimization techniques, like branch-and-bound, mathematical programming or constraint programming are also increasingly popular. On the front of applications, metaheuristics are now used to find high-quality solutions to an ever-growing number of complex, ill-defined real-world problems, in particular combinatorial ones. This handbook should continue to be a great reference for researchers, graduate students, as well as practitioners interested in metaheuristics.
Download or read book Swarm Intelligence written by Marco Dorigo and published by Springer Science & Business Media. This book was released on 2010-09-02 with total page 599 pages. Available in PDF, EPUB and Kindle. Book excerpt: These proceedings contain the papers presented at ANTS 2010, the 7th Int- national Conference on Swarm Intelligence, organized by IRIDIA, CoDE, U- versitéLibre de Bruxelles,Brussels, Belgium, during September 8–10,2010.The ANTS series started in 1998 with the First International Workshop on Ant Colony Optimization (ANTS 1998), which attracted more than 50 participants. Since then ANTS, which is held bi-annually, has gradually become an inter- tional forum for researchers in the wider ?eld of swarm intelligence. In the past (since 2004), this development has been acknowledged by the inclusion of the term“SwarmIntelligence” (nextto“AntColonyOptimization”)intheconference title. This year's ANTS conference was o?cially devoted to the ?eld of swarm intelligence as a whole, without any bias towards speci?c research directions. As a result, the title of the conference was changed to “The International Conf- ence on SwarmIntelligence.” This name change is already in place this year,and future ANTS conferences will continue to use the new title. Thisvolumecontainsthebestpapersselectedoutof99submissions.Ofthese, 28 were accepted as full-length papers, while 27 were accepted as short papers. This corresponds to an overall acceptance rate of 56%. Also included in this volume are 14 extended abstracts. Of the full-length papers, 15 were selected for oral presentation at the c- ference. All other contributions, including short papers and extended abstracts, werepresentedin the formof poster presentations.Following the conference,the journal Swarm Intelligence will publish extended versions of some of the best papers presented at the conference.
Download or read book Engineering Stochastic Local Search Algorithms Designing Implementing and Analyzing Effective Heuristics written by Thomas Stützle and published by Springer. This book was released on 2007-08-22 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the International Workshop on Engineering Stochastic Local Search Algorithms. Inside the volume, readers will find twelve full papers as well as nine short papers. Topics include methodological developments, behavior of SLS algorithms, search space analysis, algorithm performance, tuning procedures, AI/OR techniques, and dynamic behavior.
Download or read book Computational Intelligence written by Christine L. Mumford and published by Springer Science & Business Media. This book was released on 2009-07-21 with total page 726 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about synergy in computational intelligence (CI). It is a c- lection of chapters that covers a rich and diverse variety of computer-based techniques, all involving some aspect of computational intelligence, but each one taking a somewhat pragmatic view. Many complex problems in the real world require the application of some form of what we loosely call “intel- gence”fortheirsolution. Fewcanbesolvedbythenaiveapplicationofasingle technique, however good it is. Authors in this collection recognize the li- tations of individual paradigms, and propose some practical and novel ways in which di?erent CI techniques can be combined with each other, or with more traditional computational techniques, to produce powerful probl- solving environments which exhibit synergy, i. e. , systems in which the whole 1 is greater than the sum of the parts . Computational intelligence is a relatively new term, and there is some d- agreement as to its precise de?nition. Some practitioners limit its scope to schemes involving evolutionary algorithms, neural networks, fuzzy logic, or hybrids of these. For others, the de?nition is a little more ?exible, and will include paradigms such as Bayesian belief networks, multi-agent systems, case-based reasoning and so on. Generally, the term has a similar meaning to the well-known phrase “Arti?cial Intelligence” (AI), although CI is p- ceived moreas a “bottom up” approachfrom which intelligent behaviour can emerge,whereasAItendstobestudiedfromthe“topdown”,andderivefrom pondering upon the “meaning of intelligence”. (These and other key issues will be discussed in more detail in Chapter 1.
Download or read book Progress in Artificial Intelligence written by Goreti Marreiros and published by Springer Nature. This book was released on 2022-09-12 with total page 818 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 21st EPIA Conference on Artificial Intelligence, EPIA 2022, which took place in Lisbon, Portugal, in August/September 2022. The 64 papers presented in this volume were carefully reviewed and selected from 85 submissions. They were organized in topical sections as follows: AI4IS - Artificial Intelligence for Industry and Societies; AIL - Artificial Intelligence and Law; AIM - Artificial Intelligence in Medicine; AIPES - Artificial Intelligence in Power and Energy Systems; AITS - Artificial Intelligence in Transportation Systems; AmIA - Ambient Intelligence and Affective Environments; GAI - General AI; IROBOT - Intelligent Robotics; KDBI - Knowledge Discovery and Business Intelligence; KRR - Knowledge Representation and Reasoning; MASTA - Multi-Agent Systems: Theory and Applications; TeMA - Text Mining and Applications.
Download or read book Multimodal Optimization by Means of Evolutionary Algorithms written by Mike Preuss and published by Springer. This book was released on 2015-11-27 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary algorithms, and global optimization. The author explains niching in evolutionary algorithms and its benefits; he examines their suitability for use as diagnostic tools for experimental analysis, especially for detecting problem (type) properties; and he measures and compares the performances of niching and canonical EAs using different benchmark test problem sets. His work consolidates the recent successes in this domain, presenting and explaining use cases, algorithms, and performance measures, with a focus throughout on the goals of the optimization processes and a deep understanding of the algorithms used. The book will be useful for researchers and practitioners in the area of computational intelligence, particularly those engaged with heuristic search, multimodal optimization, evolutionary computing, and experimental analysis.
Download or read book Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems written by Andrea Lodi and published by Springer Science & Business Media. This book was released on 2010-06 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2010, held in Bologna, Italy, in June 2010. The 18 revised full papers and 17 revised short papers presented together with the extended abstracts of 3 invited talks were carefully reviewed and selected from 72 submissions. The papers are focused on both theoretical and practical, application-oriented issues and present current research with a special focus on the integration and hybridization of the approaches of constraint programming, artificial intelligence, and operations research technologies for solving large scale and complex real life combinatorial optimization problems.
