عنوان

Memetic computation :

(Number (ISBN

3030027295

(Number (ISBN

3030027309

(Number (ISBN

9783030027292

(Number (ISBN

9783030027308

Erroneous ISBN

3030027287

Erroneous ISBN

9783030027285

Title Proper

Memetic computation :

General Material Designation

[Book]

Other Title Information

the mainspring of knowledge transfer in a data-driven optimization era /

First Statement of Responsibility

Abhishek Gupta, Yew-Soon Ong.

Place of Publication, Distribution, etc.

Cham, Switzerland :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

[2019]

Specific Material Designation and Extent of Item

1 online resource

Series Title

Adaptation, learning, and optimization ;

Volume Designation

volume 21

Text of Note

Includes bibliographical references.

Text of Note

Introduction: Rise of Memetics in Computing -- Canonical Memetic Algorithms -- Data-Driven Adaptation in Memetic Algorithms -- The Memetic Automaton -- Sequential Knowledge Transfer across Problems -- Multitask Knowledge Transfer across Problems -- Future Direction: Meme Space Evolutions.

0

Text of Note

This book bridges the widening gap between two crucial constituents of computational intelligence: the rapidly advancing technologies of machine learning in the digital information age, and the relatively slow-moving field of general-purpose search and optimization algorithms. With this in mind, the book serves to offer a data-driven view of optimization, through the framework of memetic computation (MC). The authors provide a summary of the complete timeline of research activities in MC - beginning with the initiation of memes as local search heuristics hybridized with evolutionary algorithms, to their modern interpretation as computationally encoded building blocks of problem-solving knowledge that can be learned from one task and adaptively transmitted to another. In the light of recent research advances, the authors emphasize the further development of MC as a simultaneous problem learning and optimization paradigm with the potential to showcase human-like problem-solving prowess; that is, by equipping optimization engines to acquire increasing levels of intelligence over time through embedded memes learned independently or via interactions. In other words, the adaptive utilization of available knowledge memes makes it possible for optimization engines to tailor custom search behaviors on the fly - thereby paving the way to general-purpose problem-solving ability (or artificial general intelligence). In this regard, the book explores some of the latest concepts from the optimization literature, including, the sequential transfer of knowledge across problems, multitasking, and large-scale (high dimensional) search, systematically discussing associated algorithmic developments that align with the general theme of memetics. The presented ideas are intended to be accessible to a wide audience of scientific researchers, engineers, students, and optimization practitioners who are familiar with the commonly used terminologies of evolutionary computation. A full appreciation of the mathematical formalizations and algorithmic contributions requires an elementary background in probability, statistics, and the concepts of machine learning. A prior knowledge of surrogate-assisted/Bayesian optimization techniques is useful, but not essential.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9783030027292

Title

Memetic computation.

International Standard Book Number

9783030027285

Evolutionary computation.

Machine learning.

Memetics.

Evolutionary computation.

Machine learning.

Memetics.

COM004000

UYQ

UYQ

Number

Edition

Class number

Book number

Gupta, Abhishek

Ong, Yew Soon

Date of Transaction

20200823074536.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y