1997 IEEE International Conference On Evolutionary Computation (ICEC). PDF Download

Author:
Publisher:
ISBN: 9780780339491
Category :
Languages : en
Pages :

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Author: IEEE.
Publisher:
ISBN: 9780780339507
Category :
Languages : en
Pages : 724

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Author: Institute of Electrical and Electronics Engineers
Publisher:
ISBN:
Category :
Languages : en
Pages : 724

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Author: Youssef Mejdoub
Publisher: Springer Nature
ISBN: 3031704118
Category :
Languages : en
Pages : 442

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Author: Thomas, J. Joshua
Publisher: IGI Global
ISBN: 1799811948
Category : Computers
Languages : en
Pages : 355

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Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Author: Holger H. Hoos
Publisher: Morgan Kaufmann
ISBN: 1558608729
Category : Business & Economics
Languages : en
Pages : 678

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Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally difficult problems. Offering a systematic treatment of SLS algorithms, this book examines the general concepts and specific instances of SLS algorithms and considers their development, analysis and application.

Author: Ashish Ghosh
Publisher: Springer
ISBN: 354077467X
Category : Technology & Engineering
Languages : en
Pages : 169

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The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.

Author: Nimrod Megiddo
Publisher: Springer Science & Business Media
ISBN: 3540262245
Category : Computers
Languages : en
Pages : 496

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This book constitutes the refereed proceedings of the First International Conference on Algorithmic Applications in Management, AAIM 2005, held in Xian, China in June 2005. The 46 revised full papers presented together with abstracts of 2 invited talks were carefully reviewed and selected from 140 submissions. Among the topics addressed are approximation, complexity, automatic timetabling, scheduling algorithms, game-theoretic algorithms, economic equilibrium computation, graph computations, network algorithms, computational geometry, combinatorial optimization, sequencing, network management, data mining, Knapsack problems, etc.

Author: Kwang Y. Lee
Publisher: John Wiley & Sons
ISBN: 0470225858
Category : Technology & Engineering
Languages : en
Pages : 616

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This book explores how developing solutions with heuristic tools offers two major advantages: shortened development time and more robust systems. It begins with an overview of modern heuristic techniques and goes on to cover specific applications of heuristic approaches to power system problems, such as security assessment, optimal power flow, power system scheduling and operational planning, power generation expansion planning, reactive power planning, transmission and distribution planning, network reconfiguration, power system control, and hybrid systems of heuristic methods.

Author: Marco Dorigo
Publisher: MIT Press
ISBN: 9780262042192
Category : Computers
Languages : en
Pages : 324

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An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.