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Numerical and Evolutionary Optimization 2018

Author : Adriana Lara

Publisher : MDPI

Page : 230 pages

File Size : 30,85 MB

Release : 2019-11-19

Category : Technology & Engineering

ISBN : 3039218166

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Book Description

This book was established after the 6th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.



Numerical and Evolutionary Optimization – NEO 2017

Author : Leonardo Trujillo

Publisher : Springer

Page : 320 pages

File Size : 20,97 MB

Release : 2018-07-12

Category : Technology & Engineering

ISBN : 3319961047

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Book Description

This book features 15 chapters based on the Numerical and Evolutionary Optimization (NEO 2017) workshop, held from September 27 to 29 in the city of Tijuana, Mexico. The event gathered researchers from two complimentary fields to discuss the theory, development and application of state-of-the-art techniques to address search and optimization problems. The lively event included 7 invited talks and 64 regular talks covering a wide range of topics, from evolutionary computer vision and machine learning with evolutionary computation, to set oriented numeric and steepest descent techniques. Including research submitted by the NEO community, the book provides informative and stimulating material for future research in the field.



Data-Driven Evolutionary Optimization

Author : Yaochu Jin

Publisher : Springer Nature

Page : 393 pages

File Size : 16,46 MB

Release : 2021-06-28

Category : Computers

ISBN : 3030746402

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Book Description

Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.



Archiving Strategies for Evolutionary Multi-objective Optimization Algorithms

Author : Oliver Schütze

Publisher : Springer Nature

Page : 242 pages

File Size : 27,63 MB

Release : 2021-01-04

Category : Technology & Engineering

ISBN : 3030637735

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Book Description

This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad framework that involves all existing stochastic search algorithms and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms, and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the field of multi-objective optimization.



Evolutionary Computation

Author : Gai-Ge Wang

Publisher : MDPI

Page : 424 pages

File Size : 13,57 MB

Release : 2019-11-28

Category : Technology & Engineering

ISBN : 3039219286

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Book Description

Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological world, mimic the physiological structure and bodily functions of the organism, imitate the behavior of the animal's group, mimic the characteristics of human thought, language, and memory processes, or mimic the physical phenomena of nature, hoping to simulate the wisdom of nature and humanity enables an optimal solution to the problem and solves an acceptable solution in an acceptable time. Computational intelligent algorithms have received extensive attention at home and abroad, and have become an important research direction of artificial intelligence and computer science. This book will introduce the application of intelligent optimization algorithms in detail from the aspects of computational intelligence, job shop scheduling problems, multi-objective optimization problems, and machine learning



The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018)

Author : Aboul Ella Hassanien

Publisher : Springer

Page : 726 pages

File Size : 49,29 MB

Release : 2018-01-25

Category : Technology & Engineering

ISBN : 3319746901

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Book Description

This book presents the refereed proceedings of the third International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2018, held in Cairo, Egypt, on February 22–24, 2018, and organized by the Scientific Research Group in Egypt (SRGE). The papers cover current research in machine learning, big data, Internet of Things, biomedical engineering, fuzzy logic, security, and intelligence swarms and optimization.



Smart Applications with Advanced Machine Learning and Human-Centred Problem Design

Author : D. Jude Hemanth

Publisher : Springer Nature

Page : 801 pages

File Size : 42,4 MB

Release : 2023-01-01

Category : Technology & Engineering

ISBN : 303109753X

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Book Description

This book brings together the most recent, quality research papers accepted and presented in the 3rd International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2021) held in Antalya, Turkey between 1-3 October 2021. Objective of the content is to provide important and innovative research for developments-improvements within different engineering fields, which are highly interested in using artificial intelligence and applied mathematics. As a collection of the outputs from the ICAIAME 2021, the book is specifically considering research outcomes including advanced use of machine learning and careful problem designs on human-centred aspects. In this context, it aims to provide recent applications for real-world improvements making life easier and more sustainable for especially humans. The book targets the researchers, degree students, and practitioners from both academia and the industry.



Data Driven Methods for Civil Structural Health Monitoring and Resilience

Author : Mohammad Noori

Publisher : CRC Press

Page : 459 pages

File Size : 20,71 MB

Release : 2023-10-26

Category : Technology & Engineering

ISBN : 1000965589

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Book Description

Data Driven Methods for Civil Structural Health Monitoring and Resilience: Latest Developments and Applications provides a comprehensive overview of data-driven methods for structural health monitoring (SHM) and resilience of civil engineering structures, mostly based on artificial intelligence or other advanced data science techniques. This allows existing structures to be turned into smart structures, thereby allowing them to provide intelligible information about their state of health and performance on a continuous, relatively real-time basis. Artificial-intelligence-based methodologies are becoming increasingly more attractive for civil engineering and SHM applications; machine learning and deep learning methods can be applied and further developed to transform the available data into valuable information for engineers and decision makers.



Computer Supported Cooperative Work and Social Computing

Author : Yuqing Sun

Publisher : Springer Nature

Page : 650 pages

File Size : 48,51 MB

Release : 2022-07-19

Category : Computers

ISBN : 9811945462

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Book Description

The two-volume set CCIS 1491 and 1492 constitutes the refereed post-conferenceproceedings of the 16th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2021, held in Xiangtan, China, November 26–28, 2021. The conference was held in a hybrid mode i.e. online and on-site in Xiangtan due to the COVID-19 crisis. The 65 revised full papers and 22 revised short papers were carefully reviewed and selected from 242 submissions. The papers are organized in the following topical sections: Volume I: Collaborative Mechanisms, Models, Approaches, Algorithms and Systems; Cooperative Evolutionary Computation and Human-like Intelligent Collaboration; Domain-Specific Collaborative Applications; Volume II: Crowd Intelligence and Crowd Cooperative Computing; Social Media and Online Communities.



Differential Evolution: From Theory to Practice

Author : B. Vinoth Kumar

Publisher : Springer Nature

Page : 389 pages

File Size : 43,38 MB

Release : 2022-01-25

Category : Technology & Engineering

ISBN : 9811680825

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Book Description

This book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. Differential evolution is a population-based meta-heuristic technique for global optimization capable of handling non-differentiable, non-linear and multi-modal objective functions. Many advances have been made recently in differential evolution, from theory to applications. This book comprises contributions which include theoretical developments in DE, performance comparisons of DE, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications. The book is useful for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research and natural computing.