Frontiers in Genetics Algorithm Theory and Applications [electronic resource] / edited by Mahdi Khosravy, Neeraj Gupta, Olaf Witkowski.

Colaborador(es): Khosravy, Mahdi [editor.] | Gupta, Neeraj [editor.] | Witkowski, Olaf [editor.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Springer Tracts in Nature-Inspired ComputingEditor: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: XIV, 287 p. 90 illus., 48 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789819981076Tema(s): Computational intelligence | Algorithms | Mathematical optimization | Building information modeling | Computational Intelligence | Algorithms | Optimization | Building Information ModelingFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 006.3 Clasificación LoC:Q342Recursos en línea: Libro electrónicoTexto
Contenidos:
Evolutionary Computation: Perspectives on Past and Future -- Genetic Algorithms: A Technical Implementation of Natural Evolution -- Mating In Genetic Algorithm With Application In System Identification -- A Hybrid Biased Random-Key Genetic Algorithm for the Container Relocation Problem -- Genetic Algorithm and Its Applications in Power Systems.
En: Springer Nature eBookResumen: This book reviews recent advances in theory and applications of genetic algorithm (GA). The book is composed of five parts; Part 1 of the book involves the chapters about the advances in GA theory. Part 2 concerns applications in health, society, and economy. Part 3 has an inclusive focus on application in power systems, and Part 4 concerns the applications of GA in electrical vehicle industries. Finally, Part 5 includes applications in signal and image processing.
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Evolutionary Computation: Perspectives on Past and Future -- Genetic Algorithms: A Technical Implementation of Natural Evolution -- Mating In Genetic Algorithm With Application In System Identification -- A Hybrid Biased Random-Key Genetic Algorithm for the Container Relocation Problem -- Genetic Algorithm and Its Applications in Power Systems.

This book reviews recent advances in theory and applications of genetic algorithm (GA). The book is composed of five parts; Part 1 of the book involves the chapters about the advances in GA theory. Part 2 concerns applications in health, society, and economy. Part 3 has an inclusive focus on application in power systems, and Part 4 concerns the applications of GA in electrical vehicle industries. Finally, Part 5 includes applications in signal and image processing.

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