عنوان

Memetic computation :

شابک

3030027295

شابک

3030027309

شابک

9783030027292

شابک

9783030027308

شابک اشتباه

3030027287

شابک اشتباه

9783030027285

عنوان اصلي

Memetic computation :

نام عام مواد

[Book]

ساير اطلاعات عنواني

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

نام نخستين پديدآور

Abhishek Gupta, Yew-Soon Ong.

محل نشرو پخش و غیره

Cham, Switzerland :

نام ناشر، پخش کننده و غيره

Springer,

تاریخ نشرو بخش و غیره

[2019]

نام خاص و کميت اثر

1 online resource

عنوان فروست

Adaptation, learning, and optimization ;

مشخصه جلد

volume 21

متن يادداشت

Includes bibliographical references.

متن يادداشت

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.

بدون عنوان

0

متن يادداشت

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.

منبع سفارش / آدرس اشتراک

Springer Nature

شماره انبار

com.springer.onix.9783030027292

عنوان

Memetic computation.

شماره استاندارد بين المللي کتاب و موسيقي

9783030027285

موضوع مستند نشده

Evolutionary computation.

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Memetics.

موضوع مستند نشده

Evolutionary computation.

موضوع مستند نشده

Machine learning.

موضوع مستند نشده

Memetics.

موضوع مستند نشده

COM004000

موضوع مستند نشده

UYQ

موضوع مستند نشده

UYQ

شماره

ويراست

شماره رده

نشانه اثر

مستند نام اشخاص تاييد نشده

Gupta, Abhishek

مستند نام اشخاص تاييد نشده

Ong, Yew Soon

تاريخ عمليات

20200823074536.0

قواعد فهرست نويسي ( بخش توصيفي )

pn

نام الکترونيکي

نوع ماده

[Book]

تكميل شده

Y