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Books > Computing & IT > Applications of computing > Artificial intelligence
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Memetic Computation - The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era (Hardcover, 1st ed. 2019)
Loot Price: R4,233
Discovery Miles 42 330
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Memetic Computation - The Mainspring of Knowledge Transfer in a Data-Driven Optimization Era (Hardcover, 1st ed. 2019)
Series: Adaptation, Learning, and Optimization, 21
Expected to ship within 12 - 19 working days
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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.
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