Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 53,55
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New. In English.
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
EUR 173,51
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New. In.
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 173,50
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : New.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 196
Quantité disponible : 1 disponible(s)
Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
EUR 212,88
Quantité disponible : 1 disponible(s)
Ajouter au panierEtat : New.
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
EUR 195,91
Quantité disponible : Plus de 20 disponibles
Ajouter au panierEtat : As New. Unread book in perfect condition.
Vendeur : Books Puddle, New York, NY, Etats-Unis
EUR 236,44
Quantité disponible : 4 disponible(s)
Ajouter au panierEtat : New. 1st ed. 2023 edition NO-PA16APR2015-KAP.
Vendeur : Books Puddle, New York, NY, Etats-Unis
EUR 237,83
Quantité disponible : 4 disponible(s)
Ajouter au panierEtat : New.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 188,90
Quantité disponible : 1 disponible(s)
Ajouter au panierTaschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain's ability to generalize in optimization - particularly in population-based evolutionary algorithms - have received little attention to date.Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems,each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks.This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 188,08
Quantité disponible : 1 disponible(s)
Ajouter au panierBuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - A remarkable facet of the human brain is its ability to manage multiple tasks with apparent simultaneity. Knowledge learned from one task can then be used to enhance problem-solving in other related tasks. In machine learning, the idea of leveraging relevant information across related tasks as inductive biases to enhance learning performance has attracted significant interest. In contrast, attempts to emulate the human brain's ability to generalize in optimization - particularly in population-based evolutionary algorithms - have received little attention to date.Recently, a novel evolutionary search paradigm, Evolutionary Multi-Task (EMT) optimization, has been proposed in the realm of evolutionary computation. In contrast to traditional evolutionary searches, which solve a single task in a single run, evolutionary multi-tasking algorithm conducts searches concurrently on multiple search spaces corresponding to different tasks or optimization problems,each possessing a unique function landscape. By exploiting the latent synergies among distinct problems, the superior search performance of EMT optimization in terms of solution quality and convergence speed has been demonstrated in a variety of continuous, discrete, and hybrid (mixture of continuous and discrete) tasks.This book discusses the foundations and methodologies of developing evolutionary multi-tasking algorithms for complex optimization, including in domains characterized by factors such as multiple objectives of interest, high-dimensional search spaces and NP-hardness.
Langue: anglais
Edité par Springer-Nature New York Inc, 2023
ISBN 10 : 9811956499 ISBN 13 : 9789811956492
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 270,61
Quantité disponible : 2 disponible(s)
Ajouter au panierHardcover. Etat : Brand New. 229 pages. 9.25x6.10x0.79 inches. In Stock.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 246,16
Quantité disponible : 4 disponible(s)
Ajouter au panierEtat : New. Print on Demand.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 246,99
Quantité disponible : 4 disponible(s)
Ajouter au panierEtat : New. Print on Demand.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 249,36
Quantité disponible : 4 disponible(s)
Ajouter au panierEtat : New. PRINT ON DEMAND.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 249,96
Quantité disponible : 4 disponible(s)
Ajouter au panierEtat : New. PRINT ON DEMAND.