Adapting Evolutionary Approaches For Optimization In Dynamic Environments
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| Author | : Jürgen Branke |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 217 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461509114 |
Evolutionary Algorithms (EAs) have grown into a mature field of research in optimization, and have proven to be effective and robust problem solvers for a broad range of static real-world optimization problems. Yet, since they are based on the principles of natural evolution, and since natural evolution is a dynamic process in a changing environment, EAs are also well suited to dynamic optimization problems. Evolutionary Optimization in Dynamic Environments is the first comprehensive work on the application of EAs to dynamic optimization problems. It provides an extensive survey on research in the area and shows how EAs can be successfully used to continuously and efficiently adapt a solution to a changing environment, find a good trade-off between solution quality and adaptation cost, find robust solutions whose quality is insensitive to changes in the environment, find flexible solutions which are not only good but that can be easily adapted when necessary. All four aspects are treated in this book, providing a holistic view on the challenges and opportunities when applying EAs to dynamic optimization problems. The comprehensive and up-to-date coverage of the subject, together with details of latest original research, makes Evolutionary Optimization in Dynamic Environments an invaluable resource for researchers and professionals who are dealing with dynamic and stochastic optimization problems, and who are interested in applying local search heuristics, such as evolutionary algorithms.
| Author | : Abdunnaser Younes |
| Publisher | : |
| Total Pages | : |
| Release | : 2006 |
| Genre | : |
| ISBN | : |
| Author | : Reza Vafashoar |
| Publisher | : Springer Nature |
| Total Pages | : 377 |
| Release | : 2020-07-24 |
| Genre | : Technology & Engineering |
| ISBN | : 3030531414 |
This book highlights both theoretical and applied advances in cellular learning automata (CLA), a type of hybrid computational model that has been successfully employed in various areas to solve complex problems and to model, learn, or simulate complicated patterns of behavior. Owing to CLA’s parallel and learning abilities, it has proven to be quite effective in uncertain, time-varying, decentralized, and distributed environments. The book begins with a brief introduction to various CLA models, before focusing on recently developed CLA variants. In turn, the research areas related to CLA are addressed as bibliometric network analysis perspectives. The next part of the book presents CLA-based solutions to several computer science problems in e.g. static optimization, dynamic optimization, wireless networks, mesh networks, and cloud computing. Given its scope, the book is well suited for all researchers in the fields of artificial intelligence and reinforcement learning.
| Author | : Shengxiang Yang |
| Publisher | : Springer |
| Total Pages | : 479 |
| Release | : 2013-11-18 |
| Genre | : Technology & Engineering |
| ISBN | : 3642384161 |
This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time. Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.
| Author | : Slim Bechikh |
| Publisher | : Springer |
| Total Pages | : 187 |
| Release | : 2016-08-09 |
| Genre | : Technology & Engineering |
| ISBN | : 3319429787 |
This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.
| Author | : Ashish Ghosh |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 1001 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 3642189652 |
This book provides a collection of fourty articles containing new material on both theoretical aspects of Evolutionary Computing (EC), and demonstrating the usefulness/success of it for various kinds of large-scale real world problems. Around 23 articles deal with various theoretical aspects of EC and 17 articles demonstrate the success of EC methodologies. These articles are written by leading experts of the field from different countries all over the world.
| Author | : Shengxiang Yang |
| Publisher | : Springer |
| Total Pages | : 614 |
| Release | : 2007-04-03 |
| Genre | : Technology & Engineering |
| ISBN | : 3540497749 |
This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums.
| Author | : Donald Davendra |
| Publisher | : Springer |
| Total Pages | : 294 |
| Release | : 2016-02-04 |
| Genre | : Technology & Engineering |
| ISBN | : 3319281615 |
This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA.
| Author | : Shengxiang Yang |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 614 |
| Release | : 2007-03-07 |
| Genre | : Mathematics |
| ISBN | : 3540497722 |
This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums.
| Author | : Günther R. Raidl |
| Publisher | : Springer |
| Total Pages | : 582 |
| Release | : 2004-03-09 |
| Genre | : Computers |
| ISBN | : 3540246533 |
Evolutionary Computation (EC) deals with problem solving, optimization, and machine learning techniques inspired by principles of natural evolution and - netics. Just from this basic de?nition, it is clear that one of the main features of theresearchcommunityinvolvedinthestudyofitstheoryandinitsapplications is multidisciplinarity. For this reason, EC has been able to draw the attention of an ever-increasing number of researchers and practitioners in several ?elds. In its 6-year-long activity, EvoNet, the European Network of Excellence in Evolutionary Computing, has been the natural reference and incubator for that multifaceted community. EvoNet has provided logistic and material support for thosewhowerealreadyinvolvedinECbut,inthe?rstplace,ithashadacritical role in favoring the signi?cant growth of the EC community and its interactions with longer-established ones. The main instrument that has made this possible has been the series of events, ?rst organized in 1998, that have spanned over both theoretical and practical aspects of EC. Ever since 1999, the present format, in which the EvoWorkshops, a collection of workshops on the most application-oriented aspects of EC, act as satellites of a core event, has proven to be very successful and very representative of the multi-disciplinarity of EC. Up to 2003, the core was represented by EuroGP, the main European event dedicated to Genetic Programming. EuroGP has been joined as the main event in 2004 by EvoCOP, formerly part of EvoWorkshops, which has become the European Conference on Evolutionary Computation in Combinatorial Optimization.
