Construct, Merge, Solve & Adapt (Record no. 88324)

000 -LEADER
fixed length control field 04528nam a22006135i 4500
001 - CONTROL NUMBER
control field 978-3-031-60103-3
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730172449.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240619s2024 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031601033
-- 978-3-031-60103-3
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Blum, Christian.
245 10 - TITLE STATEMENT
Title Construct, Merge, Solve & Adapt
Sub Title A Hybrid Metaheuristic for Combinatorial Optimization /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2024.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XVI, 192 p. 58 illus., 43 illus. in color.
490 1# - SERIES STATEMENT
Series statement Computational Intelligence Methods and Applications,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Introduction to CMSA -- Self-Adaptive CMSA -- Adding Learning to CMSA -- Replacing Hard Mathematical Models with Set Covering Formulations -- Application of CMSA in the Presence of Non-Binary Variables -- Additional Research Lines Concerning CMSA.
520 ## - SUMMARY, ETC.
Summary, etc This book describes a general hybrid metaheuristic for combinatorial optimization labeled Construct, Merge, Solve & Adapt (CMSA). The general idea of standard CMSA is the following one. At each iteration, a number of valid solutions to the tackled problem instance are generated in a probabilistic way. Hereby, each of these solutions is composed of a set of solution components. The components found in the generated solutions are then added to an initially empty sub-instance. Next, an exact solver is applied in order to compute the best solution of the sub-instance, which is then used to update the sub-instance provided as input for the next iteration. In this way, the power of exact solvers can be exploited for solving problem instances much too large for a standalone application of the solver. Important research lines on CMSA from recent years are covered in this book. After an introductory chapter about standard CMSA, subsequent chapters cover a self-adaptive CMSA variant as well as a variant equipped with a learning component for improving the quality of the generated solutions over time. Furthermore, on outlining the advantages of using set-covering-based integer linear programming models for sub-instance solving, the author shows how to apply CMSA to problems naturally modelled by non-binary integer linear programming models. The book concludes with a chapter on topics such as the development of a problem-agnostic CMSA and the relation between large neighborhood search and CMSA. Combinatorial optimization problems used in the book as test cases include the minimum dominating set problem, the variable-sized bin packing problem, and an electric vehicle routing problem. The book will be valuable and is intended for researchers, professionals and graduate students working in a wide range of fields, such as combinatorial optimization, algorithmics, metaheuristics, mathematical modeling, evolutionary computing, operations research, artificial intelligence, or statistics.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-60103-3
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer Nature Switzerland :
-- Imprint: Springer,
-- 2024.
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-- computer
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-- rdamedia
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-- online resource
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-- text file
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer science.
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-- Operations research.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Management science.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer simulation.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Theory of Computation.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Operations Research, Management Science.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Modelling.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2510-1773
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