Last edited by JoJohn
Monday, February 10, 2020 | History

6 edition of Hybrid metaheuristics found in the catalog.

Hybrid metaheuristics

HM 2005 (2005 Barcelona, Spain)

Hybrid metaheuristics

second international workshop, HM 2005, Barcelona, Spain, August 29-30, 2005 : proceedings

by HM 2005 (2005 Barcelona, Spain)

  • 340 Want to read
  • 28 Currently reading

Published by Springer in Berlin .
Written in English

    Subjects:
  • Combinatorial optimization -- Congresses,
  • Heuristic programming -- Congresses

  • Edition Notes

    Other titlesHM 2005.
    StatementMaría J. Blesa ... [et al.] (eds).
    GenreCongresses.
    SeriesLecture notes in computer science -- 3636.
    ContributionsBlesa, María J., LINK (Online service)
    The Physical Object
    FormatElectronic resource
    ID Numbers
    Open LibraryOL17629199M
    ISBN 103540285350
    OCLC/WorldCa61402305

    This is explained below. Similar to this is the use of two instances of a metaheuristic: one to determine the number of the features to be selected and the second one to perform the feature selection. As a research direction, more applications of hybrid metaheuristics for different optimization problems in general and more particularly for real-word classification problems will be considered. Designing and implementing a hybrid metaheuristic involves wide knowledge about algorithms, data structure, programming, and statistics [ 3 ]. Subset problems such as the feature selection problem do not have fixed length [ 49 ]. An example problem is the travelling salesman problem where the search-space of candidate solutions grows faster than exponentially as the size of the problem increases, which makes an exhaustive search for the optimal solution infeasible.

    A first introductory chapter reviews the basic principles of local search, prominent metaheuristics, and tree search, dynamic programming, mixed integer linear programming, and constraint programming for combinatorial optimization purposes. Despite this success, it became recently evident that the focus on pure metaheuristics is restrictive when tackling particular optimization problems such as real-world and large-scale optimization problems [ 2 ]. The authors—noted experts on the topic—provide a text that can aid in the design and development of hybrid meta-heuristics to be applied to data clustering. Furthermore multiple product inventory models are often too cumbersome to be used in practice when the variety of products is very huge.

    Free shipping for individuals worldwide Usually dispatched within 3 to 5 business days. Classification consists of examining the features of a new object and then assigning it to one of the predefined set of classes. But we will give up all of mentioned assumptions and extend this version in two practical cases. However, we will consider multi product inventory models in order to show that these models can be also implemented in practice easily. If no, then the decision is to develop a hybrid metaheuristic and we will need to know the answer of the following questions. Some of them are included in the most of commercial software for business solutions.


Share this book
You might also like
rose round.

rose round.

life and reign of the Emperor Lucius Septimius Severus.

life and reign of the Emperor Lucius Septimius Severus.

American conservation

American conservation

Incontinence in old people.

Incontinence in old people.

Winning fights

Winning fights

Leonardo.

Leonardo.

education of Hindus under Muslim rule.

education of Hindus under Muslim rule.

The Drunkards looking-glass: or, A short view of their present shame and future misery.

The Drunkards looking-glass: or, A short view of their present shame and future misery.

Alexander H. Stephens on the study of the law

Alexander H. Stephens on the study of the law

Anglo-Saxon art to A.D. 900

Anglo-Saxon art to A.D. 900

Bibliographie de lhistoire du Québec et du Canada, 1981-1985 =

Bibliographie de lhistoire du Québec et du Canada, 1981-1985 =

party politics of Euroscepticism in EU member and candidate states

party politics of Euroscepticism in EU member and candidate states

autobiography of Mark Rutherford.

autobiography of Mark Rutherford.

defendant

defendant

Hybrid metaheuristics book

This has inevitably led to research that aims at combining different algorithmic components in order to design algorithms that are more powerful than the ones resulting from the implementation of pure metaheuristic strategies. A metaheuristic is a set of algorithmic concepts used for defining heuristic methods that can be applied to a variety of optimization problems with relatively few modifications in order to adapt them to particular optimization problems [ 56 ].

Many metaheuristics such as ant colony optimization algorithms, particle swarm optimization, genetic algorithms, simulated annealing, and tabu search have been used for solving the feature selection problem [ 2021 ]. The usual usage is to use them for first selecting the most relevant features from the given dataset that will be used for building the used classifier.

The answer to these questions basically depends on the objectives of the business or even manager and the strategy used to accomplish the objectives. In the light of that, we can come up with the following comments: For solving many applications, using hybrid metaheuristics was crucial to get high-quality solutions especially for real-world applications such as personnel and machine scheduling, educational timetabling, routing, cutting and packing, and protein alignment.

Which hybrid metaheuristic will work well for this optimization problem? In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms.

Metaheuristics are not problem-specific. The final prices may differ from the prices shown due to specifics of VAT rules About this book Optimization problems are of great importance in many fields. Therefore, the resulting hybrid metaheuristic will work as follows: the nature-inspired metaheuristic will identify the promising search areas from which the local search method can then determine quickly the best solutions.

Therefore, a large number of optimization algorithms were proposed to tackle them [ 56 ]. In this chapter we study an extended version of newsboy problem that considers fuzzy and rough environments and two different applications of the mentioned problem are proposed.

Hybrid Metaheuristics

This was reported in many specific conferences and workshops. The objects to be classified are generally represented by records in a dataset. They can be tackled, for example, by approximate algorithms such as metaheuristics. Chapter Preview Top Introduction For as long as managers remember, companies have tried to design an effective and efficient business model where the vital goal is to satisfy customer needs better than competitors.

Original contributors either revised or updated their work, or provided entirely new chapters. Artificial Intelligence Back cover copy Optimization problems are of great importance in many fields.

Metaheuristics: From Design to Implementation / Edition 1

The classifier is considered the basic component of any classification system, and its task is to partition the feature space into class-labeled decision regions one for each category. For example, there are several mathematical models for the inventory control in use today in which the main objective is to have a good management of inventories of raw materials, spare parts or finished goods.

Additionally, multidimensional combinatorial problems, including most design problems in engineering [12] [13] [14] such as form-finding and behavior-finding, suffer from the curse of Hybrid metaheuristics bookwhich also makes them infeasible for exhaustive search or analytical methods.

They are: 1 joint replenishment EOQ problem, 2 newsboy problem, and 3 stochastic review problem. The feature selection problem is used in many applications from choosing the most important social-economic parameters in order to identify who can return a bank loan to dealing with a chemical process and selecting the best set of ingredients.

Examples of classification Hybrid metaheuristics book are: classifying credit applications such as low, medium, or high risky, determining whether a customer with a given profile will buy a new computer, predicting which of three specific treatments a breast cancer patient should receive, determining whether a will was written by the real person or somebody else, diagnosing whether a particular illness is present or not, choosing particular contents to be displayed on a web page, determining which phone numbers correspond to fax machines, placing a new student into a particular track based on special needs, identifying whether a behavior indicates a possible terrorist threat, and spotting fraudulent insurance claims.

These are shown in Figures 6 and 7. Computer Technology Nonfiction An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering.

For each of them two different applications are provided. This interdisciplinary field is called hybrid metaheuristics which goes beyond the scope of a pure metaheuristic [ 1 ]. Three basic inventory problems, joint replenishment EOQ problem, newsboy problem, and stochastic review problem, in certain and uncertain environments such as stochastic, rough, and fuzzy environments with six different applications, are considered.

Alternatively, a metaheuristic improves a solution generated by a tree search method. Parallel metaheuristics[ edit ] A parallel metaheuristic is one which uses the techniques of parallel programming to run multiple metaheuristic searches in parallel; these may range from simple distributed schemes to concurrent search runs that interact to improve the overall solution.from book Hybrid Metaheuristics: One important class of such algorithms are metaheuristics.

The field of metaheuristic research has enjoyed a considerable popularity in the last decades. Mar 01,  · Hybrid Metaheuristics Hybrid Metaheuristics Blum, Christian The hybridization with other techniques for optimization has been one of the most interesting trends in the last years for what concerns research on metaheuristics.

In fact, in the past 10 years the focus of research on metaheuristics has notably shifted from an algorithm-oriented point of view to a problem Author: Blum, Christian. Add to Book Bag Remove from Book Bag. Saved in: Hybrid metaheuristics. Bibliographic Details; Corporate Author: SpringerLink (Online service) Other Authors: Talbi, El-Ghazali, Format: t A Unified Taxonomy of Hybrid Metaheuristics with Mathematical Programming, Constraint Programming and Machine Learning /.

Aug 19,  · The book is divided into three parts: * Part One: Introduction to Metaheuristics and Parallelism, including an Introduction to Metaheuristic Techniques, Measuring the Performance of Parallel Metaheuristics, New Technologies in Parallelism, and a head-to-head discussion on Metaheuristics and Parallelism Parallel Hybrid Metaheuristics.

Always include the URL, as this book is primarily found online. Do not include the online version numbers unless you must, as Citeseer and Google Scholar may treat each (oft-changing) version as a different book. BibTEX: @Book{ LukeMetaheuristics, author = { Sean Luke }, title = { Essentials of Metaheuristics}, edition = { second }, year.

The authors involved in this book are among the top researchers in their domain. The book is intended both to provide an overview of hybrid metaheuristics to novices of the field, and to provide researchers from the field with a collection of some of the most interesting recent atlasbowling.com by: