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This book introduces readers to an evolutionary learning approach,
specifically genetic programming (GP), for production scheduling.
The book is divided into six parts. In Part I, it provides an
introduction to production scheduling, existing solution methods,
and the GP approach to production scheduling. Characteristics of
production environments, problem formulations, an abstract GP
framework for production scheduling, and evaluation criteria are
also presented. Part II shows various ways that GP can be employed
to solve static production scheduling problems and their
connections with conventional operation research methods. In turn,
Part III shows how to design GP algorithms for dynamic production
scheduling problems and describes advanced techniques for enhancing
GP's performance, including feature selection, surrogate modeling,
and specialized genetic operators. In Part IV, the book addresses
how to use heuristics to deal with multiple, potentially
conflicting objectives in production scheduling problems, and
presents an advanced multi-objective approach with cooperative
coevolution techniques or multi-tree representations. Part V
demonstrates how to use multitask learning techniques in the
hyper-heuristics space for production scheduling. It also shows how
surrogate techniques and assisted task selection strategies can
benefit multitask learning with GP for learning heuristics in the
context of production scheduling. Part VI rounds out the text with
an outlook on the future. Given its scope, the book benefits
scientists, engineers, researchers, practitioners, postgraduates,
and undergraduates in the areas of machine learning, artificial
intelligence, evolutionary computation, operations research, and
industrial engineering.
This book introduces readers to an evolutionary learning approach,
specifically genetic programming (GP), for production scheduling.
The book is divided into six parts. In Part I, it provides an
introduction to production scheduling, existing solution methods,
and the GP approach to production scheduling. Characteristics of
production environments, problem formulations, an abstract GP
framework for production scheduling, and evaluation criteria are
also presented. Part II shows various ways that GP can be employed
to solve static production scheduling problems and their
connections with conventional operation research methods. In turn,
Part III shows how to design GP algorithms for dynamic production
scheduling problems and describes advanced techniques for enhancing
GP's performance, including feature selection, surrogate modeling,
and specialized genetic operators. In Part IV, the book addresses
how to use heuristics to deal with multiple, potentially
conflicting objectives in production scheduling problems, and
presents an advanced multi-objective approach with cooperative
coevolution techniques or multi-tree representations. Part V
demonstrates how to use multitask learning techniques in the
hyper-heuristics space for production scheduling. It also shows how
surrogate techniques and assisted task selection strategies can
benefit multitask learning with GP for learning heuristics in the
context of production scheduling. Part VI rounds out the text with
an outlook on the future. Given its scope, the book benefits
scientists, engineers, researchers, practitioners, postgraduates,
and undergraduates in the areas of machine learning, artificial
intelligence, evolutionary computation, operations research, and
industrial engineering.
This book is an effort to review the theoretical framework for the
use of alternative assessment in EFL contexts and provides a
collection of empirical research on alternative assessment for
different groups of EFL learners in various classrooms. The aim of
the book is not to simply review the past literature on alternative
assessment in EFL, rather it aims to evaluate current language
policy reforms, in which opportunities for alternative assessment
are incorporated. Recent developments and possibilities for
alternative assessment implementation in specific language skills
are encouraged. The book provides empirical findings specific to
certain sub-skills using alternative assessment strategies in EFL.
Both the theoretical framework, and the insights and implications
from the empirical research described in the book will make
alternative assessment more accessible to language teachers in the
most relevant and practical way.
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