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This book covers the most recent advances in the field of
evolutionary multiobjective optimization. With the aim of drawing
the attention of up-and coming scientists towards exciting
prospects at the forefront of computational intelligence, the
authors have made an effort to ensure that the ideas conveyed
herein are accessible to the widest audience. The book begins with
a summary of the basic concepts in multi-objective optimization.
This is followed by brief discussions on various algorithms that
have been proposed over the years for solving such problems,
ranging from classical (mathematical) approaches to sophisticated
evolutionary ones that are capable of seamlessly tackling practical
challenges such as non-convexity, multi-modality, the presence of
multiple constraints, etc. Thereafter, some of the key emerging
aspects that are likely to shape future research directions in the
field are presented. These include: optimization in dynamic
environments, multi-objective bilevel programming, handling high
dimensionality under many objectives, and evolutionary
multitasking. In addition to theory and methodology, this book
describes several real-world applications from various domains,
which will expose the readers to the versatility of evolutionary
multi-objective optimization.
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.
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.
This book discusses all aspects of money laundering, starting from
traditional approach to financial crimes to artificial
intelligence-enabled solutions. It also discusses the regulators
approach to curb financial crimes and how syndication among
financial institutions can create a robust ecosystem for monitoring
and managing financial crimes. It opens with an introduction to
financial crimes for a financial institution, the context of
financial crimes, and its various participants. Various types of
money laundering, terrorist financing, and dealing with watch list
entities are also part of the discussion. Through its twelve
chapters, the book provides an overview of ways in which financial
institutions deal with financial crimes; various IT solutions for
monitoring and managing financial crimes; data organization and
governance in the financial crimes context; machine learning and
artificial intelligence (AI) in financial crimes; customer-level
transaction monitoring system; machine learning-driven alert
optimization; AML investigation; bias and ethical pitfalls in
machine learning; and enterprise-level AI-driven Financial Crime
Investigation (FCI) unit. There is also an Appendix which contains
a detailed review of various data sciences approaches that are
popular among practitioners. The book discusses each topic through
real-life experiences. It also leverages the experience of Chief
Compliance Officers of some large organizations to showcase real
challenges that heads of large organizations face while dealing
with this sensitive topic. It thus delivers a hands-on guide for
setting up, managing, and transforming into a best-in-class
financial crimes management unit. It is thus an invaluable resource
for researchers, students, corporates, and industry watchers alike.
This book covers the most recent advances in the field of
evolutionary multiobjective optimization. With the aim of drawing
the attention of up-and coming scientists towards exciting
prospects at the forefront of computational intelligence, the
authors have made an effort to ensure that the ideas conveyed
herein are accessible to the widest audience. The book begins with
a summary of the basic concepts in multi-objective optimization.
This is followed by brief discussions on various algorithms that
have been proposed over the years for solving such problems,
ranging from classical (mathematical) approaches to sophisticated
evolutionary ones that are capable of seamlessly tackling practical
challenges such as non-convexity, multi-modality, the presence of
multiple constraints, etc. Thereafter, some of the key emerging
aspects that are likely to shape future research directions in the
field are presented. These include: optimization in dynamic
environments, multi-objective bilevel programming, handling high
dimensionality under many objectives, and evolutionary
multitasking. In addition to theory and methodology, this book
describes several real-world applications from various domains,
which will expose the readers to the versatility of evolutionary
multi-objective optimization.
Numerical Methods with MATLAB provides a highly-practical reference
work to assist anyone working with numerical methods. A wide range
of techniques are introduced, their merits discussed and fully
working MATLAB code samples supplied to demonstrate how they can be
coded and applied. Numerical methods have wide applicability across
many scientific, mathematical, and engineering disciplines and are
most often employed in situations where working out an exact answer
to the problem by another method is impractical. Numerical Methods
with MATLAB presents each topic in a concise and readable format to
help you learn fast and effectively. It is not intended to be a
reference work to the conceptual theory that underpins the
numerical methods themselves. A wide range of reference works are
readily available to supply this information. If, however, you want
assistance in applying numerical methods then this is the book for
you.
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