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The aim of this volume is to show how Fuzzy Sets and Systems can help to provide robust and adaptive heuristic optimization algorithms in a variety of situations. The book presents the state of the art and gives a broad overview on the real practical applications that Fuzzy Sets, based on heuristic algorithms, have.
This book constitutes the joint refereed proceedings of six workshops, EvoWorkshops 2003, held together with EuroGP 2003 in Essex, UK in April 2003. The 63 revised full papers presented were carefully reviewed and selected from a total of 109 submissions. In accordance with the six workshops covered , the papers are organized in topical sections on bioinformatics, combinatorial optimization, image analysis and signal processing, evolutionary music and art, evolutionary robotics, and scheduling and timetabling.
This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of Evo*2023, in April 2023, co-located with the Evo*2023 events EuroGP, EvoCOP, and EvoMUSART. The EuroGP focused on the technique of genetic programming, EvoCOP targeted evolutionary computation in combinatorial optimization, and EvoMUSART was dedicated to evolved and bio-inspired music, sound, art, and design. The EvoApplications 2023 presents papers on the different areas: Analysis of Evolutionary Computation Methods: Theory, Empirics, and Real-World Applications, Applications of Bio-inspired Techniques on Social Networks, Evolutionary Computation in Edge, Fog, and Cloud Computing, Evolutionary Computation in Image Analysis, Signal Processing, and Pattern Recognition and others.
This book discusses how to build optimization tools able to generate better future studies. It aims at showing how these tools can be used to develop an adaptive learning environment that can be used for decision making in the presence of uncertainties. The book starts with existing fuzzy techniques and multicriteria decision making approaches and shows how to combine them in more effective tools to model future events and take therefore better decisions. The first part of the book is dedicated to the theories behind fuzzy optimization and fuzzy cognitive map, while the second part presents new approaches developed by the authors with their practical application to trend impact analysis, scenario planning and strategic formulation. The book is aimed at two groups of readers, interested in linking the future studies with artificial intelligence. The first group includes social scientists seeking for improved methods for strategic prospective. The second group includes computer scientists and engineers seeking for new applications and current developments of Soft Computing methods for forecasting in social science, but not limited to this.
This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. The conference was scheduled to take place in Lisbon, Portugal, at University of Lisbon, but due to COVID-19 pandemic it was held virtually. The 173 papers were carefully reviewed and selected from 213 submissions. The papers are organized in topical sections: homage to Enrique Ruspini; invited talks; foundations and mathematics; decision making, preferences and votes; optimization and uncertainty; games; real world applications; knowledge processing and creation; machine learning I...
The two volumes set LNCS 7653 and 7654 constitutes the refereed proceedings of the 4th International Conference on Computational Collective Intelligence, ICCCI, held in Ho Chi Minh City, Vietnam, in November 2012. The 113 revised full papers presented were carefully reviewed and selected from 397 submissions. The papers are organized in topical sections on (Part I) knowledge integration; data mining for collective processing; fuzzy, modal, and collective systems; nature inspired systems; language processing systems; social networks and semantic web; agent and multi-agent systems; classification and clustering methods; modeling and optimization techniques for business intelligence; (Part II) multi-dimensional data processing; web systems; intelligent decision making; methods for scheduling; collective intelligence in web systems – web systems analysis; advanced data mining techniques and applications; cooperative problem solving; computational swarm intelligence; and semantic methods for knowledge discovery and communication
This book constitutes the thoroughly refereed post-conference proceedings of the Third International Conference on Learning and Intelligent Optimization, LION 2009 III, held in Trento, Italy, in January 2009. The 15 revised full papers, one extended abstract and two poster sessions were carefully reviewed and selected from 86 submissions for inclusion in the book. The papers cover current issues of stochastic local search methods and meta-heuristics, hybridizations of constraint and mathematical programming with meta-heuristics, supervised, unsupervised and reinforcement learning applied to heuristic search, reactive search (online self-tuning methods), algorithm portfolios and off-line tuni...
The Web has revolutionized the way we seek information on all aspects of education, entertainment, business, health and so on. The Web has evolved into a publishing medium, global electronic market and increasingly, a platform for conducting electronic commerce. A part of this success can be attributed to the tremendous advances made in the Artificial Intelligence field. The popularity of the Web has opened many opportunities to develop smart Web-based systems using artificial intelligence techniques. There exist numerous Web technology and applications that can benefit with the application of artificial intelligence techniques. It is not possible to cover them all in one book with a require...
Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets. We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement...
One of the keystones in practical metaheuristic problem-solving is the fact that tuning the optimization technique to the problem under consideration is crucial for achieving top performance. This tuning/customization is usually in the hands of the algorithm designer, and despite some methodological attempts, it largely remains a scientific art. Transferring a part of this customization effort to the algorithm itself -endowing it with smart mechanisms to self-adapt to the problem- has been a long pursued goal in the field of metaheuristics. These mechanisms can involve different aspects of the algorithm, such as for example, self-adjusting the parameters, self-adapting the functioning of int...