|
Showing 1 - 2 of
2 matches in All Departments
This book is devoted to the leading research in applying learning
automaton (LA) and heuristics for solving benchmark and real-world
optimization problems. The ever-increasing application of the LA as
a promising reinforcement learning technique in artificial
intelligence makes it necessary to provide scholars, scientists,
and engineers with a practical discussion on LA solutions for
optimization. The book starts with a brief introduction to LA
models for optimization. Afterward, the research areas related to
LA and optimization are addressed as bibliometric network analysis.
Then, LA's application in behavior control in evolutionary
computation, and memetic models of object migration automata and
cellular learning automata for solving NP hard problems are
considered. Next, an overview of multi-population methods for DOPs,
LA's application in dynamic optimization problems (DOPs), and the
function evaluation management in evolutionary multi-population for
DOPs are discussed. Highlighted benefits * Presents the latest
advances in learning automata-based optimization approaches. *
Addresses the memetic models of learning automata for solving
NP-hard problems. * Discusses the application of learning automata
for behavior control in evolutionary computation in detail. * Gives
the fundamental principles and analyses of the different concepts
associated with multi-population methods for dynamic optimization
problems.
This book is devoted to the leading research in applying learning
automaton (LA) and heuristics for solving benchmark and real-world
optimization problems. The ever-increasing application of the LA as
a promising reinforcement learning technique in artificial
intelligence makes it necessary to provide scholars, scientists,
and engineers with a practical discussion on LA solutions for
optimization. The book starts with a brief introduction to LA
models for optimization. Afterward, the research areas related to
LA and optimization are addressed as bibliometric network analysis.
Then, LA's application in behavior control in evolutionary
computation, and memetic models of object migration automata and
cellular learning automata for solving NP hard problems are
considered. Next, an overview of multi-population methods for DOPs,
LA's application in dynamic optimization problems (DOPs), and the
function evaluation management in evolutionary multi-population for
DOPs are discussed. Highlighted benefits * Presents the latest
advances in learning automata-based optimization approaches. *
Addresses the memetic models of learning automata for solving
NP-hard problems. * Discusses the application of learning automata
for behavior control in evolutionary computation in detail. * Gives
the fundamental principles and analyses of the different concepts
associated with multi-population methods for dynamic optimization
problems.
|
You may like...
It: Chapter 1
Bill Skarsgård
Blu-ray disc
R149
R49
Discovery Miles 490
Higher
Michael Buble
CD
(1)
R172
R154
Discovery Miles 1 540
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.