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Differential evolutionfrom theory to practice /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Differential evolutionedited by B. Vinoth Kumar, Diego Oliva, P. N. Suganthan.
Reminder of title:	from theory to practice /
other author:	Kumar, B. Vinoth.
Published:	Singapore :Springer Singapore :2022.
Description:	xiv, 381 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Evolution equations.
Online resource:	https://doi.org/10.1007/978-981-16-8082-3
ISBN:	9789811680823$q(electronic bk.)

Differential evolutionfrom theory to practice /
Differential evolutionfrom theory to practice /[electronic resource] :edited by B. Vinoth Kumar, Diego Oliva, P. N. Suganthan. - Singapore :Springer Singapore :2022. - xiv, 381 p. :ill., digital ;24 cm. - Studies in computational intelligence,v. 10091860-9503 ;. - Studies in computational intelligence ;v. 216..

Analysis of Structural Bias in Differential Evolution Configurations -- Spherical Model of Population Dynamics in Differential Evolution -- Reinforcement Learning-based Differential Evolution for Global Optimization -- Analytical Study on the Role of Scale Factor Parameter of Differential Evolution Algorithm on its Convergence Nature -- The Trap of Sisyphus Work in Differential Evolution and How to Avoid It -- Investigations on Distributed Differential Evolution Framework with Fault Tolerance Mechanisms -- Differential Evolution for Water Management Problems -- Sobol Sequence Based MOSADE Algorithm for Multi-Objective Design of Water Distribution Networks -- A Comparative Study on Parameter Estimation of Covid Epidemiological Models using Differential Evolution Algorithm -- Applications of Differential Evolution in Electric Power Systems -- Detection of Heavy Sandstorm Regions using Composite Differential Evolution Algorithm -- A Hybrid Artificial Differential Evolution Gorilla Troops Optimizer for High Dimensional Optimization Problems.

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.

ISBN: 9789811680823$q(electronic bk.)

Standard No.: 10.1007/978-981-16-8082-3doiSubjects--Topical Terms:

199049
Evolution equations.

LC Class. No.: QA377.3 / .D54 2022

Dewey Class. No.: 515.35

Differential evolutionfrom theory to practice /
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505 0 $a Analysis of Structural Bias in Differential Evolution Configurations -- Spherical Model of Population Dynamics in Differential Evolution -- Reinforcement Learning-based Differential Evolution for Global Optimization -- Analytical Study on the Role of Scale Factor Parameter of Differential Evolution Algorithm on its Convergence Nature -- The Trap of Sisyphus Work in Differential Evolution and How to Avoid It -- Investigations on Distributed Differential Evolution Framework with Fault Tolerance Mechanisms -- Differential Evolution for Water Management Problems -- Sobol Sequence Based MOSADE Algorithm for Multi-Objective Design of Water Distribution Networks -- A Comparative Study on Parameter Estimation of Covid Epidemiological Models using Differential Evolution Algorithm -- Applications of Differential Evolution in Electric Power Systems -- Detection of Heavy Sandstorm Regions using Composite Differential Evolution Algorithm -- A Hybrid Artificial Differential Evolution Gorilla Troops Optimizer for High Dimensional Optimization Problems.
520 $a 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.
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