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| {{citations missing|date=December 2009}}
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| '''Guided Local Search''' is a [[metaheuristic]] search method. A meta-heuristic method is a method that sits on top of a [[local search (optimization)|local search algorithm]] to change its behaviour.
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| Guided Local Search builds up penalties during a search. It uses penalties to help local search algorithms escape from local minima and plateaus. When the given local search algorithm settles in a local optimum, GLS modifies the objective function using a specific scheme (explained below). Then the local search will operate using an augmented objective function, which is designed to bring the search out of the local optimum. The key is in the way that the objective function is modified.
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| ==Overview==
| | It involves expertise and knowledge of various tools and technologies used for creating websites. Offshore expert Word - Press developers high level of interactivity, accessibility, functionality and usability of our website can add custom online to using. Should you go with simple HTML or use a platform like Wordpress. Keep reading for some great Word - Press ideas you can start using today. You can easily customize the titles of the posts in Word - Press blog in a way that only title comes in the new post link and not the date or category of posts. <br><br>Right starting from social media support to search engine optimization, such plugins are easily available within the Word - Press open source platform. The higher your blog ranks on search engines, the more likely people will find your online marketing site. It allows Word - Press users to easily use HTML5 the element enable native video playback within the browser. It primarily lays emphasis on improving the search engine results of your website whenever a related query is typed in the search box. This can be done by using a popular layout format and your unique Word - Press design can be achieved in other elements of the blog. <br><br>Just ensure that you hire experienced Word - Press CMS developer who is experienced enough to perform the task of Word - Press customization to get optimum benefits of Word - Press CMS. Word - Press has ensured the users of this open source blogging platform do not have to troubleshoot on their own, or seek outside help. When you loved this informative article and you would like to receive more information relating to [http://footystar.co.uk/profile/evdunkley backup plugin] assure visit our web page. Setting Up Your Business Online Using Free Wordpress Websites. You or your web designer can customize it as per your specific needs. Socrates: (link to ) Originally developed for affiliate marketers, I've used this theme again and again to develop full-fledged web sites that include static pages, squeeze pages, and a blog. <br><br>Word - Press has plenty of SEO benefits over Joomla and Drupal. And, that is all the opposition events with nationalistic agenda in favor of the individuals of Pakistan marching collectively in the battle in opposition to radicalism. This allows for keeping the content editing toolbar in place at all times no matter how far down the page is scrolled. Giant business organizations can bank on enterprise solutions to incorporate latest web technologies such as content management system etc, yet some are looking for economical solutions. Digital digital cameras now function gray-scale configurations which allow expert photographers to catch images only in black and white. <br><br>Website security has become a major concern among individuals all over the world. Being a Plugin Developer, it is important for you to know that development of Word - Press driven website should be done only when you enable debugging. It's not a secret that a lion share of activity on the internet is takes place on the Facebook. ) Remote Login: With the process of PSD to Wordpress conversion comes the advantage of flexibility. For your information, it is an open source web content management system. |
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| ===Solution features===
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| To apply GLS, solution features must be defined for the given problem. Solution features are defined to distinguish between solutions with different characteristics, so that regions of similarity around local optima can be recognized and avoided. The choice of solution features depends on the type of problem, and also to a certain extent on the local search algorithm. For each feature <math>f_i</math> a cost function <math>c_i</math> is defined.
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| Each feature is also associated with a penalty <math>p_i</math> (initially set to 0) to record the number of occurrences of the feature in local minima.
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| The features and costs often come directly from the objective function. For example, in the traveling salesman problem, “whether the tour goes directly from city X to city Y” can be defined to be a feature. The distance between X and Y can be defined to be the cost. In the SAT and weighted MAX-SAT problems, the features can be “whether clause C satisfied by the current assignments”.
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| At the implementation level, we define for each feature <math>i</math> an Indicator Function <math>I_i</math> indicating whether the feature is present in the current solution or not. <math>I_i</math> is 1 when solution <math>x</math> exhibits property <math>i</math>, 0 otherwise.
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| ===Selective penalty modifications===
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| GLS computes the utility of penalising each feature. When the Local Search algorithm returns a [[maxima and minima|local minimum]] x, GLS penalizes all those features (through increments to the penalty of the features) present in that solution which have maximum utility, <math>\operatorname{util}(x,i)</math>, as defined below.
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| : <math>\operatorname{util}(x,i) = I_i(x)\frac{c_i(x)}{1+p_i}.</math>
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| The idea is to penalise features that have high costs, although the utility of doing so decreases as the feature is penalised more and more often. | |
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| ===Searching through an augmented cost function===
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| GLS uses an augmented cost function (defined below), to allow it to guide the Local Search algorithm out of the local minimum, through penalising features present in that local minimum. The idea is to make the local minimum more costly than the surrounding search space, where these features are not present.
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| : <math>g(x) = f(x) + \lambda a \sum_{1\leq i\leq m} I_i(x) p_i </math>
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| The parameter λ may be used to alter the intensification of the search for solutions. A higher value for λ will result in a more diverse search, where plateaus and basins are searched more coarsely; a low value will result in a more intensive search for the solution, where the plateaus and basins in the search landscape are searched in finer detail. The coefficient <math>a</math> is used to make the penalty part of the objective function balanced relative to changes in the objective function and is problem specific. A simple heuristic for setting <math>a</math> is simply to record the average change in objective function up until the first local minimum, and then set <math>a</math> to this value divided by the number of GLS features in the problem instance.
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| ===Extensions of Guided Local Search===
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| [http://www.bracil.net/CSP/papers/Mills-GLS-PhD2002.pdf Mills (2002)] has described an Extended Guided Local Search (EGLS) which utilises random moves and an aspiration criterion designed specifically for penalty based schemes. The resulting algorithm improved the robustness of GLS over a range of parameter settings, particularly in the case of the [[quadratic assignment problem]]. A general version of the GLS algorithm, using a min-conflicts based hill climber (Minton et al. 1992) and based partly on GENET for constraint satisfaction and optimisation, has also been implemented in the [http://www.bracil.net/CSP/cacp/ Computer Aided Constraint Programming project].
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| [http://www.bracil.net/CSP/papers/Alsheddy-PhD-2011.pdf Alsheddy (2011)] extended Guided Local Search to [[multi-objective optimization]], and demonstrated its use in staff empowerment in scheduling.
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| ==Related work==
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| GLS was built on GENET, which was developed by Chang Wang, Edward Tsang and Andrew Davenport.
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| The [[breakout method]] is very similar to GENET. It was designed for [[constraint satisfaction]].
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| [[Tabu search]] is a class of search methods which can be instantiated to specific methods. GLS can be seen as a special case of [[Tabu search]].
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| By sitting GLS on top of genetic algorithm, Tung-leng Lau introduced the Guided Genetic Programming (GGA) algorithm. It was successfully applied to the general assignment problem (in scheduling), processors configuration problem (in electronic design) and a set of radio-link frequency assignment problems (an abstracted military application).
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| Choi et al. cast GENET as a Lagrangian search.
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| ==Bibliography==
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| * Alsheddy, A., Empowerment scheduling: a multi-objective optimization approach using Guided Local Search, PhD Thesis, School of Computer Science and Electronic Engineering, University of Essex, 2011 [http://www.bracil.net/CSP/papers/Alsheddy-PhD-2011.pdf]
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| * Choi, K.M.F., Lee, J.H.M. & Stuckey, P.J., A Lagrangian Resconstruction of GENET, Artificial Intelligence, 2000, 123(1-2), 1-39
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| * Davenport A., Tsang E.P.K., Kangmin Zhu & C J Wang, GENET: A connectionist architecture for solving constraint satisfaction problems by iterative improvement, Proc., AAAI, 1994, p.325-330 [ftp://ftp.essex.ac.uk/pub/csp/aaai94.ps.Z]
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| * Lau, T.L. & Tsang, E.P.K., Solving the processor configuration problem with a mutation-based genetic algorithm, International Journal on Artificial Intelligence Tools (IJAIT), World Scientific, Vol.6, No.4, December 1997, 567-585
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| * Lau, T.L. & Tsang, E.P.K., Guided genetic algorithm and its application to radio link frequency assignment problems, Constraints, Vol.6, No.4, 2001, 373-398 [http://www.bracil.net/CSP/papers/LauTsang-Rlfap-Constraints2001.pdf]
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| * Lau, T.L. & Tsang, E.P.K., The guided genetic algorithm and its application to the general assignment problems, IEEE 10th International Conference on Tools with Artificial Intelligence (ICTAI'98), Taiwan, November 1998
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| * Mills, P. & Tsang, E.P.K., Guided local search for solving SAT and weighted MAX-SAT problems, Journal of Automated Reasoning, Special Issue on Satisfiability Problems, Kluwer, Vol.24, 2000, 205-223 [http://www.bracil.net/CSP/papers/MilTsa-Glssat-Sat2000.pdf]
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| * Mills, P. & Tsang, E.P.K. & Ford, J., Applying an Extended Guided Local Search on the Quadratic Assignment Problem, Annals of Operations Research, Kluwer Academic Publishers, Vol.118, 2003, 121-135 [http://www.bracil.net/CSP/papers/MiTsFo-GlsQap-AnOr2002.pdf]
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| * Minton, S., Johnston, M., Philips, A.B. & Laird, P., Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems, Artificial Intelligence (Special Volume on Constraint Based Reasoning), Vol.58, Nos.1-3 1992, 161-205
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| * Tsang, E.P.K. & Voudouris, C., Fast local search and guided local search and their application to British Telecom's workforce scheduling problem, Operations Research Letters, Elsevier Science Publishers, Amsterdam, Vol.20, No.3, March 1997, 119-127 [http://www.bracil.net/CSP/papers/TsaVou-GlsWfs-ORL1997.pdf]
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| * Voudouris, C, Guided local search for combinatorial optimisation problems, PhD Thesis, Department of Computer Science, University of Essex, Colchester, UK, July, 1997 [http://cswww.essex.ac.uk/staff/voudcx/doc/phd_pdf.zip]
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| * Voudouris, C., Guided Local Search—An illustrative example in function optimisation, BT Technology Journal, Vol.16, No.3, July 1998, 46-50 [ftp://ftp.essex.ac.uk/pub/csp/Voud-GlsF6-Bttj98.pdf]
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| * Voudouris, C. & Tsang, E.P.K., Guided Local Search and its application to the Travelling Salesman Problem, European Journal of Operational Research, Anbar Publishing, Vol.113, Issue 2, March 1999, 469-499 [http://www.bracil.net/CSP/papers/CSM-247.ps.Z]
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| * Voudouris, C. & Tsang, E.P.K., Guided local search joins the elite in discrete optimisation, DIMACS Series in Discrete Mathematics and Theoretical Computer Science Volume 57, 2001, 29-39 [ftp://ftp.essex.ac.uk/pub/csp/VouTsa-GLSOpt-Dimacs98.ps.Z]
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| * Voudouris, C. & Tsang, E.P.K., Guided local search, in F. Glover (ed.), Handbook of metaheuristics, Kluwer, 2003, 185-218 [http://www.bracil.net/CSP/papers/VouTsa-Gls-MetaHeuristic2003.pdf]
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| * Voudouris, C., Tsang, E.P.K. & Alsheddy, A., Guided local search, Chapter 11, in M. Gendreau & J-Y Potvin (ed.), Handbook of Metaheuristics, Springer, 2010, 321-361
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| ==External links==
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| * [http://www.bracil.net/CSP/gls.html Guided Local Search Home Page]
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| [[Category:Optimization algorithms and methods]]
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| [[Category:Heuristics]]
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| {{Optimization algorithms}}
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