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.
