Static Environment Vs Dynamic Environment
Download Static Environment Vs Dynamic Environment PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Static Environment Vs Dynamic Environment book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Foundations in Grammatical Evolution for Dynamic Environments
Author: Ian Dempsey
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-04-07
Dynamic environments abound, encompassing many real-world problems in fields as diverse as finance, engineering, biology and business. A vibrant research literature has emerged which takes inspiration from evolutionary processes to develop problem-solvers for these environments. 'Foundations in Grammatical Evolution for Dynamic Environments' is a cutting edge volume illustrating current state of the art in applying grammar-based evolutionary computation to solve real-world problems in dynamic environments. The book provides a clear introduction to dynamic environments and the types of change that can occur. This is followed by a detailed description of evolutionary computation, concentrating on the powerful Grammatical Evolution methodology. It continues by addressing fundamental issues facing all Evolutionary Algorithms in dynamic problems, such as how to adapt and generate constants, how to enhance evolvability and maintain diversity. Finally, the developed methods are illustrated with application to the real-world dynamic problem of trading on financial time-series. The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, who are seeking to apply grammar-based evolutionary algorithms to solve problems in dynamic environments. 'Foundations in Grammatical Evolution for Dynamic Environments' is the second book dedicated to the topic of Grammatical Evolution.
The Dynamics Between Entrepreneurship, Environment and Education
Author: Alain Fayolle
language: en
Publisher: Edward Elgar Publishing
Release Date: 2008-01-01
The book. . . does exactly what the editors say it does, it delivers a rich variety of European research. . . it comprehensively inspires important and worthwhile dialogue. Anne M.J. Smith, International Journal of Entrepreneurship & Innovation This overview of the current research in the field will provide academics, researchers and policy makers with new insights through which to understand the contextual dimensions and the broadening aspects of the current state-of-the-art in European research. International Journal of Sustainability in Higher Education The authors of the chapters offer a broad variety of topics and approaches that significantly contribute to the understanding of changes in society, and the diversity of the contexts in which entrepreneurship occurs. I am convinced that the book will inspire a dialogue, not only among researchers, but also between research and policy-makers in order that the changes and dynamics of society be better understood. From the foreword by Hans Landström, Lund University, Sweden This book introduces the expanding European dialogue between entrepreneurship, environment and education. It considers the shape, dimensions and horizon of this multidisciplinary landscape in entrepreneurship research. The striking differences and contradictions in entrepreneurial activities, readiness and innovativeness within European countries and the proactive attitude and activities of European competitors impose a demand for a better understanding of the complex dynamics. The Dynamics between Entrepreneurship, Environment and Education reflects how the European landscape of entrepreneurship research is now more complex than ever. It presents an overview of the current state of entrepreneurship research in Europe and also reflects on the future directions of research in this field. The dynamics between entrepreneurship and society are evaluated, and the discussion is then continued from an education perspective. The authors also focus on the ability and capability of different kinds of ventures to compete in different contexts. This comprehensive overview of the current research in the field will provide academics, researchers and policy-makers with new insights through which to understand the contextual dimensions and the broadening aspects of the current state-of-the-art in European research.
Evolutionary Optimization in Dynamic Environments
Author: Jürgen Branke
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
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.