› › Swarm Evolutionary And Memetic Computing And Fuzzy And Neural Computing

Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing

Author : Aleš Zamuda

Publisher : Springer Nature

Page : 224 pages

File Size : 27,56 MB

Release : 2020-01-02

Category : Computers

ISBN : 3030378381

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Book Description

This volume constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2019, and 5th International Conference on Fuzzy and Neural Computing, FANCCO 2019, held in Maribor, Slovenia, in July 2019. The 18 full papers presented in this volume were carefully reviewed and selected from a total of 31 submissions for inclusion in the proceedings. The papers cover a wide range of topics in swarm, evolutionary, memetic and other intelligent computing algorithms and their real world applications in problems selected from diverse domains of science and engineering.



Swarm, Evolutionary, and Memetic Computing

Author : Bijaya Ketan Panigrahi

Publisher : Springer

Page : 696 pages

File Size : 47,42 MB

Release : 2013-12-12

Category : Computers

ISBN : 3319037560

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Book Description

The two-volume set LNCS 8297 and LNCS 8298 constitutes the proceedings of the 4th International Conference on Swarm, Evolutionary and Memetic Computing, SEMCCO 2013, held in Chennai, India, in December 2013. The total of 123 papers presented in this volume was carefully reviewed and selected for inclusion in the proceedings. They cover cutting-edge research on swarm, evolutionary and memetic computing, neural and fuzzy computing and its application.



Swarm, Evolutionary, and Memetic Computing, Part II

Author : Bijata Ketan Panigraphi

Publisher : Springer Science & Business Media

Page : 353 pages

File Size : 12,5 MB

Release : 2011-12-07

Category : Computers

ISBN : 364227241X

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Book Description

These two volumes, LNCS 7076 and LNCS 7077, constitute the refereed proceedings of the Second International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2011, held in Visakhapatnam, India, in December 2011. The 124 revised full papers presented in both volumes were carefully reviewed and selected from 422 submissions. The papers explore new application areas, feature new bio-inspired algorithms for solving specific hard optimization problems, and review the latest progresses in the cutting-edge research with swarm, evolutionary, and memetic computing in both theoretical and practical aspects.



Medical Diagnosis Using Artificial Neural Networks

Author : Moein, Sara

Publisher : IGI Global

Page : 326 pages

File Size : 22,64 MB

Release : 2014-06-30

Category : Medical

ISBN : 146666147X

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Book Description

Advanced conceptual modeling techniques serve as a powerful tool for those in the medical field by increasing the accuracy and efficiency of the diagnostic process. The application of artificial intelligence assists medical professionals to analyze and comprehend a broad range of medical data, thus eliminating the potential for human error. Medical Diagnosis Using Artificial Neural Networks introduces effective parameters for improving the performance and application of machine learning and pattern recognition techniques to facilitate medical processes. This book is an essential reference work for academicians, professionals, researchers, and students interested in the relationship between artificial intelligence and medical science through the use of informatics to improve the quality of medical care.



Data Democracy

Author : Feras A. Batarseh

Publisher : Academic Press

Page : 268 pages

File Size : 46,75 MB

Release : 2020-01-21

Category : Science

ISBN : 0128189398

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Book Description

Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering provides a manifesto to data democracy. After reading the chapters of this book, you are informed and suitably warned! You are already part of the data republic, and you (and all of us) need to ensure that our data fall in the right hands. Everything you click, buy, swipe, try, sell, drive, or fly is a data point. But who owns the data? At this point, not you! You do not even have access to most of it. The next best empire of our planet is one who owns and controls the world's best dataset. If you consume or create data, if you are a citizen of the data republic (willingly or grudgingly), and if you are interested in making a decision or finding the truth through data-driven analysis, this book is for you. A group of experts, academics, data science researchers, and industry practitioners gathered to write this manifesto about data democracy. - The future of the data republic, life within a data democracy, and our digital freedoms - An in-depth analysis of open science, open data, open source software, and their future challenges - A comprehensive review of data democracy's implications within domains such as: healthcare, space exploration, earth sciences, business, and psychology - The democratization of Artificial Intelligence (AI), and data issues such as: Bias, imbalance, context, and knowledge extraction - A systematic review of AI methods applied to software engineering problems



Self-Organizing Migrating Algorithm

Author : Donald Davendra

Publisher : Springer

Page : 294 pages

File Size : 22,76 MB

Release : 2016-02-04

Category : Technology & Engineering

ISBN : 3319281615

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Book Description

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.



Metaheuristic Computation: A Performance Perspective

Author : Erik Cuevas

Publisher : Springer Nature

Page : 281 pages

File Size : 43,13 MB

Release : 2020-10-05

Category : Technology & Engineering

ISBN : 3030581004

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Book Description

This book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. Metaheuristic search methods are so numerous and varied in terms of design and potential applications; however, for such an abundant family of optimization techniques, there seems to be a question which needs to be answered: Which part of the design in a metaheuristic algorithm contributes more to its better performance? Several works that compare the performance among metaheuristic approaches have been reported in the literature. Nevertheless, they suffer from one of the following limitations: (A)Their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. (B) Their conclusions consider only the comparison of their final results which cannot evaluate the nature of a good or bad balance between exploration and exploitation. The objective of this book is to compare the performance of various metaheuristic techniques when they are faced with complex optimization problems extracted from different engineering domains. The material has been compiled from a teaching perspective.



Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics

Author : Oscar Castillo

Publisher : Springer

Page : 193 pages

File Size : 27,72 MB

Release : 2014-09-20

Category : Technology & Engineering

ISBN : 331910960X

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Book Description

This book describes recent advances on fuzzy logic augmentation of nature-inspired optimization metaheuristics and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in two main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic augmentation of nature-inspired optimization metaheuristics, which basically consists of papers that propose new optimization algorithms enhanced using fuzzy systems. The second part contains papers with the main theme of application of optimization algorithms, which are basically papers using nature-inspired techniques to achieve optimization of complex optimization problems in diverse areas of application.



Recent Developments and the New Direction in Soft-Computing Foundations and Applications

Author : Shahnaz N. Shahbazova

Publisher : Springer Nature

Page : 555 pages

File Size : 49,12 MB

Release : 2020-07-10

Category : Technology & Engineering

ISBN : 3030471241

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Book Description

This book gathers authoritative contributions in the field of Soft Computing. Based on selected papers presented at the 7th World Conference on Soft Computing, which was held on May 29–31, 2018, in Baku, Azerbaijan, it describes new theoretical advances, as well as cutting-edge methods and applications. New theories and algorithms in fuzzy logic, cognitive modeling, graph theory and metaheuristics are discussed, and applications in data mining, social networks, control and robotics, geoscience, biomedicine and industrial management are described. This book offers a timely, broad snapshot of recent developments, including thought-provoking trends and challenges that are yielding new research directions in the diverse areas of Soft Computing.



Evolutionary Computation & Swarm Intelligence

Author : Fabio Caraffini

Publisher : MDPI

Page : 286 pages

File Size : 39,45 MB

Release : 2020-11-25

Category : Technology & Engineering

ISBN : 3039434543

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Book Description

The vast majority of real-world problems can be expressed as an optimisation task by formulating an objective function, also known as cost or fitness function. The most logical methods to optimise such a function when (1) an analytical expression is not available, (2) mathematical hypotheses do not hold, and (3) the dimensionality of the problem or stringent real-time requirements make it infeasible to find an exact solution mathematically are from the field of Evolutionary Computation (EC) and Swarm Intelligence (SI). The latter are broad and still growing subjects in Computer Science in the study of metaheuristic approaches, i.e., those approaches which do not make any assumptions about the problem function, inspired from natural phenomena such as, in the first place, the evolution process and the collaborative behaviours of groups of animals and communities, respectively. This book contains recent advances in the EC and SI fields, covering most themes currently receiving a great deal of attention such as benchmarking and tunning of optimisation algorithms, their algorithm design process, and their application to solve challenging real-world problems to face large-scale domains.