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This book presents recent advances in automated machine learning
(AutoML) and automated algorithm design and indicates the future
directions in this fast-developing area. Methods have been
developed to automate the design of neural networks, heuristics and
metaheuristics using techniques such as metaheuristics, statistical
techniques, machine learning and hyper-heuristics. The book first
defines the field of automated design, distinguishing it from the
similar but different topics of automated algorithm configuration
and automated algorithm selection. The chapters report on the
current state of the art by experts in the field and include
reviews of AutoML and automated design of search, theoretical
analyses of automated algorithm design, automated design of control
software for robot swarms, and overfitting as a benchmark and
design tool. Also covered are automated generation of constructive
and perturbative low-level heuristics, selection hyper-heuristics
for automated design, automated design of deep-learning approaches
using hyper-heuristics, genetic programming hyper-heuristics with
transfer knowledge and automated design of classification
algorithms. The book concludes by examining future research
directions of this rapidly evolving field. The information
presented here will especially interest researchers and
practitioners in the fields of artificial intelligence,
computational intelligence, evolutionary computation and
optimisation.
This introduction to the field of hyper-heuristics presents the
required foundations and tools and illustrates some of their
applications. The authors organized the 13 chapters into three
parts. The first, hyper-heuristic fundamentals and theory, provides
an overview of selection constructive, selection perturbative,
generation constructive and generation perturbative
hyper-heuristics, and then a formal definition of hyper-heuristics.
The chapters in the second part of the book examine applications of
hyper-heuristics in vehicle routing, nurse rostering, packing and
examination timetabling. The third part of the book presents
advanced topics and then a summary of the field and future research
directions. Finally the appendices offer details of the HyFlex
framework and the EvoHyp toolkit, and then the definition, problem
model and constraints for the most tested combinatorial
optimization problems. The book will be of value to graduate
students, researchers, and practitioners.
This introduction to the field of hyper-heuristics presents the
required foundations and tools and illustrates some of their
applications. The authors organized the 13 chapters into three
parts. The first, hyper-heuristic fundamentals and theory, provides
an overview of selection constructive, selection perturbative,
generation constructive and generation perturbative
hyper-heuristics, and then a formal definition of hyper-heuristics.
The chapters in the second part of the book examine applications of
hyper-heuristics in vehicle routing, nurse rostering, packing and
examination timetabling. The third part of the book presents
advanced topics and then a summary of the field and future research
directions. Finally the appendices offer details of the HyFlex
framework and the EvoHyp toolkit, and then the definition, problem
model and constraints for the most tested combinatorial
optimization problems. The book will be of value to graduate
students, researchers, and practitioners.
This book presents recent advances in automated machine learning
(AutoML) and automated algorithm design and indicates the future
directions in this fast-developing area. Methods have been
developed to automate the design of neural networks, heuristics and
metaheuristics using techniques such as metaheuristics, statistical
techniques, machine learning and hyper-heuristics. The book first
defines the field of automated design, distinguishing it from the
similar but different topics of automated algorithm configuration
and automated algorithm selection. The chapters report on the
current state of the art by experts in the field and include
reviews of AutoML and automated design of search, theoretical
analyses of automated algorithm design, automated design of control
software for robot swarms, and overfitting as a benchmark and
design tool. Also covered are automated generation of constructive
and perturbative low-level heuristics, selection hyper-heuristics
for automated design, automated design of deep-learning approaches
using hyper-heuristics, genetic programming hyper-heuristics with
transfer knowledge and automated design of classification
algorithms. The book concludes by examining future research
directions of this rapidly evolving field. The information
presented here will especially interest researchers and
practitioners in the fields of artificial intelligence,
computational intelligence, evolutionary computation and
optimisation.
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Advances in Nature and Biologically Inspired Computing - Proceedings of the 7th World Congress on Nature and Biologically Inspired Computing (NaBIC2015) in Pietermaritzburg, South Africa, held December 01-03, 2015 (Paperback, 1st ed. 2016)
Nelishia Pillay, Andries P. Engelbrecht, Ajith Abraham, Mathys C. Du Plessis, Vaclav Snasel, …
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R5,866
Discovery Miles 58 660
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Ships in 10 - 15 working days
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World Congress on Nature and Biologically Inspired Computing
(NaBIC) is organized to discuss the state-of-the-art as well as to
address various issues with respect to Nurturing Intelligent
Computing Towards Advancement of Machine Intelligence. This Volume
contains the papers presented in the Seventh World Congress
(NaBIC'15) held in Pietermaritzburg, South Africa during December
01-03, 2015. The 39 papers presented in this Volume were carefully
reviewed and selected. The Volume would be a valuable reference to
researchers, students and practitioners in the computational
intelligence field.
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