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This book is aimed at undergraduate and graduate students in applied mathematics or computer science, as a tool for solving real-world design problems. The present work covers fundamentals in multi-objective optimization and applications in mathematical and engineering system design using a new optimization strategy, namely the Self-Adaptive Multi-objective Optimization Differential Evolution (SA-MODE) algorithm. This strategy is proposed in order to reduce the number of evaluations of the objective function through dynamic update of canonical Differential Evolution parameters (population size, crossover probability and perturbation rate). The methodology is applied to solve mathematical functions considering test cases from the literature and various engineering systems design, such as cantilevered beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others.
A concise and practical introduction to the foundations and engineering principles of self-adaptation Though it has recently gained significant momentum, the topic of self-adaptation remains largely under-addressed in academic and technical literature. This book changes that. Using a systematic and holistic approach, An Introduction to Self-adaptive Systems: A Contemporary Software Engineering Perspective provides readers with an accessible set of basic principles, engineering foundations, and applications of self-adaptation in software-intensive systems. It places self-adaptation in the context of techniques like uncertainty management, feedback control, online reasoning, and machine learning while acknowledging the growing consensus in the software engineering community that self-adaptation will be a crucial enabling feature in tackling the challenges of new, emerging, and future systems. The author combines cutting-edge technical research with basic principles and real-world insights to create a practical and strategically effective guide to self-adaptation. He includes features such as: An analysis of the foundational engineering principles and applications of self-adaptation in different domains, including the Internet-of-Things, cloud computing, and cyber-physical systems End-of-chapter exercises at four different levels of complexity and difficulty An accompanying author-hosted website with slides, selected exercises and solutions, models, and code Perfect for researchers, students, teachers, industry leaders, and practitioners in fields that directly or peripherally involve software engineering, as well as those in academia involved in a class on self-adaptivity, this book belongs on the shelves of anyone with an interest in the future of software and its engineering.
Describing a new optimization algorithm, the “Teaching-Learning-Based Optimization (TLBO),” in a clear and lucid style, this book maximizes reader insights into how the TLBO algorithm can be used to solve continuous and discrete optimization problems involving single or multiple objectives. As the algorithm operates on the principle of teaching and learning, where teachers influence the quality of learners’ results, the elitist version of TLBO algorithm (ETLBO) is described along with applications of the TLBO algorithm in the fields of electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics and biotechnology. The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms.
Tagged arithmetic system for use with FORTRAN programs.
The mostly German psychologists contributing here contend that people secure personal continuity throughout their life span by a combination of active attempts at regulating their development on the one hand, and flexible adjustment of the self to unalterable changes both in their social and physical environment and in such personal attributes as p.
Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its newest applications.
The disciplines of science and engineering rely heavily on the forecasting of prospective constraints for concepts that have not yet been proven to exist, especially in areas such as artificial intelligence. Obtaining quality solutions to the problems presented becomes increasingly difficult due to the number of steps required to sift through the possible solutions, and the ability to solve such problems relies on the recognition of patterns and the categorization of data into specific sets. Predictive modeling and optimization methods allow unknown events to be categorized based on statistics and classifiers input by researchers. The Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering is a critical reference source that provides comprehensive information on the use of optimization techniques and predictive models to solve real-life engineering and science problems. Through discussions on techniques such as robust design optimization, water level prediction, and the prediction of human actions, this publication identifies solutions to developing problems and new solutions for existing problems, making this publication a valuable resource for engineers, researchers, graduate students, and other professionals.
Daniel Lückehe presents different approaches to optimize locations of multiple wind turbines on a topographical map. The author succeeds in significantly improving placement solutions by employing optimization heuristics. He proposes various real-world scenarios that represent real planning situations. Advanced evolutionary heuristics for the turbine placement optimization create not only highly optimized solutions but also significantly different solutions to give decision-makers optimal choices. As a matter of fact, wind turbines play an important role towards green energy supply. An optimal location is essential to achieve the highest possible energy efficiency.
The authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications. The monograph gives an insight into the research in the ?elds of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow - ponentially. Much of the data is implicitly or explicitly imprecise. Database discovery seeks to discover noteworthy, unrecognized associations between the data items in the existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. The rate at which the data is storedis growing at a phenomenal rate. Asaresult,traditionaladhocmixturesofstatisticaltechniquesanddata managementtools are no longer adequate for analyzing this vast collection of data. Severaldomainswherelargevolumesofdataarestoredincentralizedor distributeddatabasesincludesapplicationslikeinelectroniccommerce,bio- formatics, computer security, Web intelligence, intelligent learning database systems,?nance,marketing,healthcare,telecommunications,andother?elds. E?cient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the ca- bility of computers to search huge amounts of data in a fast and e?ective manner. However,the data to be analyzed is imprecise and a?icted with - certainty. In the case of heterogeneous data sources such as text and video, the data might moreover be ambiguous and partly con?icting. Besides, p- terns and relationships of interest are usually approximate. Thus, in order to make the information mining process more robust it requires tolerance toward imprecision, uncertainty and exceptions.