Gotowe bibliografie tematyczne / Stochastic local search

Gotowa bibliografia na temat „Stochastic local search”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 20 lutego 2023

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Artykuły w czasopismach na temat "Stochastic local search"

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Mouhoub, Malek. "Stochastic local search for incremental SAT". International Journal of Knowledge-based and Intelligent Engineering Systems 9, nr 3 (13.09.2005): 191–95. http://dx.doi.org/10.3233/kes-2005-9303.

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CHEN, Gong-gui. "Particle swarm optimization with stochastic local search". Journal of Computer Applications 28, nr 1 (30.06.2008): 94–96. http://dx.doi.org/10.3724/sp.j.1087.2008.00094.

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Kastrati, Muhamet, i Marenglen Biba. "Stochastic local search: a state-of-the-art review". International Journal of Electrical and Computer Engineering (IJECE) 11, nr 1 (1.02.2021): 716. http://dx.doi.org/10.11591/ijece.v11i1.pp716-727.

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The main objective of this paper is to provide a state-of-the-art review, analyze and discuss stochastic local search techniques used for solving hard combinatorial problems. It begins with a short introduction, motivation and some basic notation on combinatorial problems, search paradigms and other relevant features of searching techniques as needed for background. In the following a brief overview of the stochastic local search methods along with an analysis of the state-of-the-art stochastic local search algorithms is given. Finally, the last part of the paper present and discuss some of the most latest trends in application of stochastic local search algorithms in machine learning, data mining and some other areas of science and engineering. We conclude with a discussion on capabilities and limitations of stochastic local search algorithms.
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Nekkaa, Messaouda, i Dalila Boughaci. "Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection". Neural Processing Letters 44, nr 1 (26.06.2015): 199–220. http://dx.doi.org/10.1007/s11063-015-9450-5.

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HA*, Meesoon. "Stochastic Local Search in Random Constraint Satisfaction Problems". New Physics: Sae Mulli 63, nr 9 (30.09.2013): 995–99. http://dx.doi.org/10.3938/npsm.63.995.

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Al-Behadili, Hayder. "Stochastic Local Search Algorithms for Feature Selection: A Review". Iraqi Journal for Electrical and Electronic Engineering 17, nr 1 (2.02.2021): 1–10. http://dx.doi.org/10.37917/ijeee.17.1.1.

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In today’s world, the data generated by many applications are increasing drastically, and finding an optimal subset of features from the data has become a crucial task. The main objective of this review is to analyze and comprehend different stochastic local search algorithms to find an optimal feature subset. Simulated annealing, tabu search, genetic programming, genetic algorithm, particle swarm optimization, artificial bee colony, grey wolf optimization, and bat algorithm, which have been used in feature selection, are discussed. This review also highlights the filter and wrapper approaches for feature selection. Furthermore, this review highlights the main components of stochastic local search algorithms, categorizes these algorithms in accordance with the type, and discusses the promising research directions for such algorithms in future research of feature selection.
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Pérez Cáceres, Leslie, Ignacio Araya i Guillermo Cabrera-Guerrero. "Stochastic Local Search for the Direct Aperture Optimisation Problem". Expert Systems with Applications 182 (listopad 2021): 115206. http://dx.doi.org/10.1016/j.eswa.2021.115206.

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Yu, Hang, Yu Zhang, Pengxing Cai, Junyan Yi, Sheng Li i Shi Wang. "Stochastic Multiple Chaotic Local Search-Incorporated Gradient-Based Optimizer". Discrete Dynamics in Nature and Society 2021 (2.12.2021): 1–16. http://dx.doi.org/10.1155/2021/3353926.

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In this study, a hybrid metaheuristic algorithm chaotic gradient-based optimizer (CGBO) is proposed. The gradient-based optimizer (GBO) is a novel metaheuristic inspired by Newton’s method which has two search strategies to ensure excellent performance. One is the gradient search rule (GSR), and the other is local escaping operation (LEO). GSR utilizes the gradient method to enhance ability of exploitation and convergence rate, and LEO employs random operators to escape the local optima. It is verified that gradient-based metaheuristic algorithms have obvious shortcomings in exploration. Meanwhile, chaotic local search (CLS) is an efficient search strategy with randomicity and ergodicity, which is usually used to improve global optimization algorithms. Accordingly, we incorporate GBO with CLS to strengthen the ability of exploration and keep high-level population diversity for original GBO. In this study, CGBO is tested with over 30 CEC2017 benchmark functions and a parameter optimization problem of the dendritic neuron model (DNM). Experimental results indicate that CGBO performs better than other state-of-the-art algorithms in terms of effectiveness and robustness.
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Kaur, Harshdeep. "LOCAL SEARCH BASED ALGORITHM FOR CVRP WITH STOCHASTIC DEMANDS". International Journal of Advanced Research in Computer Science 8, nr 7 (20.08.2017): 1087–92. http://dx.doi.org/10.26483/ijarcs.v8i7.4468.

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Caramia, Massimiliano, i Paolo Dell’Olmo. "Coupling Stochastic and Deterministic Local Search in Examination Timetabling". Operations Research 55, nr 2 (kwiecień 2007): 351–66. http://dx.doi.org/10.1287/opre.1060.0354.

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Rozprawy doktorskie na temat "Stochastic local search"

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Xu, Ruoxi. "Regression Model Stochastic Search via Local Orthogonalization". The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322589253.

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He, Jun. "Constraints for Membership in Formal Languages under Systematic Search and Stochastic Local Search". Doctoral thesis, Uppsala universitet, Avdelningen för datalogi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-196347.

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This thesis focuses on constraints for membership in formal languages under both the systematic search and stochastic local search approaches to constraint programming (CP). Such constraints are very useful in CP for the following three reasons: They provide a powerful tool for user-level extensibility of CP languages. They are very useful for modelling complex work shift regulation constraints, which exist in many shift scheduling problems. In the analysis, testing, and verification of string-manipulating programs, string constraints often arise. We show in this thesis that CP solvers with constraints for membership in formal languages are much more suitable than existing solvers used in tools that have to solve string constraints. In the stochastic local search approach to CP, we make the following two contributions: We introduce a stochastic method of maintaining violations for the regular constraint and extend our method to the automaton constraint with counters. To improve the usage of constraints for which there exists no known constant-time algorithm for neighbour evaluation, we introduce a framework of using solution neighbourhoods, and give an efficient algorithm of constructing a solution neighbourhood for the regular constraint. In the systematic search approach to CP, we make the following two contributions: We show that there may be unwanted consequences when using a propagator that may underestimate a cost of a soft constraint, as the propagator may guide the search to incorrect (non-optimum) solutions to an over-constrained problem. We introduce and compare several propagators that compute correctly the cost of the edit-distance based soft-regular constraint. We show that the context-free grammar constraint is useful and introduce an improved propagator for it.
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Balint, Adrian [Verfasser]. "Engineering stochastic local search for the satisfiability problem / Adrian Balint". Ulm : Universität Ulm. Fakultät für Ingenieurwissenschaften und Informatik, 2014. http://d-nb.info/1046623567/34.

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Chiarandini, Marco. "Stochastic Local Search Methods for Highly Constrained Combinatorial Optimisation Problems". Phd thesis, [S.l. : s.n.], 2005. https://tuprints.ulb.tu-darmstadt.de/595/1/ChiarandiniPhD.pdf.

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Graph colouring is a combinatorial optimisation problem consisting in colouring the vertices of a graph such that no vertices connected by an edge receive the same colour. The minimal number of colours for which such a colouring exists is an intrinsic property of the graph and is called chromatic number. Many real life situations, such as the frequency assignment in mobile networks or the scheduling of courses at a university, can be modelled in this way. Colouring planar graphs, such as maps can be easy, and four colours suffice, but real life systems are much more complex. When modelled by graph colouring, they entail general graphs of large size and include more sophisticated constraints than those representable by simple unweighted edges. Stochastic Local Search (SLS) methods are approximate techniques for efficiently solving these complex combinatorial optimisation problems. They typically consist of construction algorithms, iterative improvement algorithms, and meta-components, better known as metaheuristics. The first two are strongly problem dependent and require the exploitation of problem-specific knowledge, while the last are more general concepts to guide the first two components. The instantiation of SLS algorithms arises from the combination of concrete algorithmic components. This task is complex due to the many possible combinations and the need of determining a certain number of parameters. Empirical tests become then necessary to take the correct decisions. The starting point of this work is the definition of the statistical methods that are appropriate for the analysis of SLS algorithms. We argue that the assumptions for the application of parametric tests are often violated and opt for two alternative methods: permutation and rank-based tests. Our work contributes to the development of permutation tests and to their introduction in the analysis of SLS algorithms. In addition, we transfer a graphical representation of results through simultaneous confidence intervals from the parametric to the non-parametric cases. This representation has the advantage of conveying in one single graph both descriptive and inferential statistics. The developed statistical methods serve for the analysis of SLS algorithms on the graph colouring problem and one of its many possible generalisations, the set T-colouring problem. Several SLS algorithms have been proposed in the literature for the graph colouring problem but no ``unbiased'' comparison has been attempted. A similar situation holds for the set T-colouring problem. In both cases, we design new algorithms, re-implement the most prominent methods, and finally compare them in a rigorous experimental analysis. As the final step, we study SLS algorithms for solving a university course timetabling problem. The design of algorithm components stems from the knowledge gained on the graph colouring problems but the assemblage and configuration of these components is carried out with a systematic methodology. The focus in this context was on the selection of one single algorithm to submit to an algorithm competition. The methodology is presented as an engineering process characterised by the interaction of SLS components design and empirical tests. We deem that this methodological approach is appropriate for the application of SLS methods to complex real life problems. The main results are the following: on the graph colouring problem, the simple Tabu Search with one-exchange neighbourhood remains a very competitive approach and the use of a very large scale neighbourhood is not profitable; on the set T-colouring problem, the best overall algorithm is an Adaptive Iterated Greedy also based on Tabu Search with one-exchange neighbourhood which, under certain circumstances, can be further improved by a restricted exact reassignment of colours; the use of an engineering methodology based on sequential testing is particularly suitable for the application of SLS methods, as it led us to devise the algorithm whose solutions for course timetabling ranked the best out of 24 feasible submissions at the International Timetabling Competition held in 2003.
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Cai, Shaowei. "Novel Local Search Methods for Satisfiability". Thesis, Griffith University, 2015. http://hdl.handle.net/10072/366424.

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The Boolean Satisfiability Problem (SAT) is a prototypical NP-complete problem, and is central in computer science and artificial intelligence. Given a formula over a set of Boolean variables, the SAT problem tests whether an interpretation that satisfies the formula exists. Stochastic Local Search (SLS) is a simple but effective approach to SAT. In this thesis, we proposed new SLS techniques for SAT solving and developed new SLS algorithms. Using empirical evaluations, we showed that at the time they were designed our algorithms performed better than the existing state-of-the-art solvers. Moreover, our algorithms have been established as the latest state-of-the-art algorithms for several types of instances. The first idea is configuration checking (CC) for SAT. A typical CC technique is to forbid flipping a variable if since the last time it was flipped, none of its neighbouring variables has been flipped. Based on this strategy, we developed several solvers, including Swcca and CCAnr. In particular, CCAnr performed very well on crafted instances, and a hybrid solver CCAnr+glucose won the silver prize of Hard-combinatorial track in SAT Competition 2014. The second idea is the notion of multilevel properties which consider the satisfaction degree of clauses. Using the CC strategy and the second level score, we developed the CCASat solver, which won the 2012 SAT Challenge random track. We also proposed several new scoring functions, which were used to design CScoreSAT and HScoreSAT. These two solvers are particularly efficient in solving random SAT instances with long clauses, and show one to two orders of magnitude improvement than previous solvers. Thirdly, we proposed a linear make function for tie-breaking in the famous algorithm WalkSAT, leading to the WalkSATlm algorithm. Our experiments demonstrate that WalkSATlm improves WalkSAT by orders of magnitudes on random k-SAT instances with k > 3.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Integrated and Intelligent Systems
Science, Environment, Engineering and Technology
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Paquete, Luís F. "Stochastic local search algorithms for multiobjective combinatorial optimization methods and analysis". Berlin Aka, 2005. http://deposit.ddb.de/cgi-bin/dokserv?id=2770886&prov=M&dok_var=1&dok_ext=htm.

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Bin, Hussin Mohamed Saifullah. "Stochastic local search algorithms for single and bi-objective quadratic assignment problems". Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/222286.

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The study of Stochastic Local Search (SLS) algorithms is becoming more pivotal these days, due to their vast number of applications in decision making. Prior to the implementation of algorithmic knowledge for decision making, many decisions were made based on manual calculation, on the fly, or even based on guts feeling. Nowadays, such an approach is more rarely seen, especially when the decisions that need to be made are high-risk, cost intensive, or time-consuming. The increasingly often used SLS algorithms are one of the options available to assist the decision making process these days.The work discussed in this thesis concerns the study of SLS algorithms for solving the Quadratic Assignment Problem (QAP), a prominent combinatorial optimization problem, which until today is very hard to solve. Our interest is to study the behavior and performance of SLS algorithms for solving QAP instances of different characteristics, such as size, sparsity, and structure. In this study, we have also proposed new variants of SLS algorithms, inspired by existing, well-performing SLS algorithms for solving the QAP. The new variants of SLS algorithms are then further extended for solving the bi-objective QAP (bQAP).One main focus in this study is to see how the performance of algorithms scales with instance size. We have considered instances that are much larger than the ones usually used in the studies of algorithms for solving the QAP. By understanding how the algorithms perform when the instance size changes, we might be able to solve other problems effectively by considering the similarity in their characteristics to the ones of the QAP, or by seeing common trends in the relative performance of the various available SLS methods. For single objective QAP instances we found that the structure and size of instances do have a significant impact on the performance of SLS algorithms. For example, comparisons between Tabu Search (TS) and Simulated Annealing (SA) on instances with randomly generated matrices show that the overall performance of TS is better than SA, irrespective the size of instances considered. The results on a class of structured instances however show that TS performs well on small-sized instances, while on the larger ones, SA shows better results. In another experiment, Hierarchical Iterated Local Search (HILS) has shown very good results compared to several Iterated Local Search (ILS) variants. This experiment was done on a class of structured instances of size from 100 to 500. An extensive experiment on a class of structured instances of size 30 to 300 using tuned parameter settings shows that population based algorithms perform very well on most of the instance classes considered. SA however, shows very good performance especially on large-sized instances with low sparsity level. For the bQAP, the correlation between the flow matrices does have a strong effect that determines the performance of algorithms for solving them. Hybrid Simulated Annealing (HSA) clearly outperforms Hybrid Iterative Improvement (HII). When compared to Multi Objective Ant Colony Optimization (MOACO) and Strength Pareto Evolutionary Algorithm 2 (SPEA2), HSA shows very good performance, where HSA outperforms MOACO and SPEA2, especially on instances of large size, thus, offering a better scaling behavior. Based the results obtained in this study, it is possible to come up with a general idea on the suitability of SLS algorithms for solving instances with a certain characteristic. Given an unknown QAP instance, one can guess the most suitable algorithm for solving it depending on the type, size, and sparsity of the instance, while for a bQAP instance the most suitable algorithm can be guessed based on its size and correlation between the flow matrices.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
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Garattoni, Lorenzo. "Advanced stochastic local search methods for automatic design of Boolean network robots". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3443/.

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Bianchi, Leonora. "Ant colony optimization and local search for the probabilistic traveling salesman problem: a case study in stochastic combinatorial optimization". Doctoral thesis, Universite Libre de Bruxelles, 2006. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210877.

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In this thesis we focus on Stochastic combinatorial Optimization Problems (SCOPs), a wide class of combinatorial optimization problems under uncertainty, where part of the information about the problem data is unknown at the planning stage, but some knowledge about its probability distribution is assumed.

Optimization problems under uncertainty are complex and difficult, and often classical algorithmic approaches based on mathematical and dynamic programming are able to solve only very small problem instances. For this reason, in recent years metaheuristic algorithms such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and others, are emerging as successful alternatives to classical approaches.

In this thesis, metaheuristics that have been applied so far to SCOPs are introduced and the related literature is thoroughly reviewed. In particular, two properties of metaheuristics emerge from the survey: they are a valid alternative to exact classical methods for addressing real-sized SCOPs, and they are flexible, since they can be quite easily adapted to solve different SCOPs formulations, both static and dynamic. On the base of the current literature, we identify the following as the key open issues in solving SCOPs via metaheuristics:

(1) the design and integration of ad hoc, fast and effective objective function approximations inside the optimization algorithm;

(2) the estimation of the objective function by sampling when no closed-form expression for the objective function is available, and the study of methods to reduce the time complexity and noise inherent to this type of estimation;

(3) the characterization of the efficiency of metaheuristic variants with respect to different levels of stochasticity in the problem instances.

We investigate the above issues by focusing in particular on a SCOP belonging to the class of vehicle routing problems: the Probabilistic Traveling Salesman Problem (PTSP). For the PTSP, we consider the Ant Colony Optimization metaheuristic and we design efficient local search algorithms that can enhance its performance. We obtain state-of-the-art algorithms, but we show that they are effective only for instances above a certain level of stochasticity, otherwise it is more convenient to solve the problem as if it were deterministic.

The algorithmic variants based on an estimation of the objective function by sampling obtain worse results, but qualitatively have the same behavior of the algorithms based on the exact objective function, with respect to the level of stochasticity. Moreover, we show that the performance of algorithmic variants based on ad hoc approximations is strongly correlated with the absolute error of the approximation, and that the effect on local search of ad hoc approximations can be very degrading.

Finally, we briefly address another SCOP belonging to the class of vehicle routing problems: the Vehicle Routing Problem with Stochastic Demands (VRPSD). For this problem, we have implemented and tested several metaheuristics, and we have studied the impact of integrating in them different ad hoc approximations.


Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished

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Goodson, Justin Christopher. "Solution methodologies for vehicle routing problems with stochastic demand". Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/675.

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We present solution methodologies for vehicle routing problems (VRPs) with stochastic demand, with a specific focus on the vehicle routing problem with stochastic demand (VRPSD) and the vehicle routing problem with stochastic demand and duration limits (VRPSDL). The VRPSD and the VRPSDL are fundamental problems underlying many operational challenges in the fields of logistics and supply chain management. We model the VRPSD and the VRPSDL as large-scale Markov decision processes. We develop cyclic-order neighborhoods, a general methodology for solving a broad class of VRPs, and use this technique to obtain static, fixed route policies for the VRPSD. We develop pre-decision, post-decision, and hybrid rollout policies for approximate dynamic programming (ADP). These policies lay a methodological foundation for solving large-scale sequential decision problems and provide a framework for developing dynamic routing policies. Our dynamic rollout policies for the VRPSDL significantly improve upon a method frequently implemented in practice. We also identify circumstances in which our rollout policies appear to offer little or no benefit compared to this benchmark. These observations can guide managerial decision making regarding when the use of our procedures is justifiable. We also demonstrate that our methodology lends itself to real-time implementation, thereby providing a mechanism to make high-quality, dynamic routing decisions for large-scale operations. Finally, we consider a more traditional ADP approach to the VRPSDL by developing a parameterized linear function to approximate the value functions corresponding to our problem formulation. We estimate parameters via a simulation-based algorithm and show that initializing parameter values via our rollout policies leads to significant improvements. However, we conclude that additional research is required to develop a parametric ADP methodology comparable or superior to our rollout policies.
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Książki na temat "Stochastic local search"

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Braziunas, Darius. Stochastic local search for POMDP controllers. Ottawa: National Library of Canada, 2003.

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Thomas, Stützle, red. Stochastic local search: Foundations and applications. San Francisco, CA: Morgan Kaufmann Publishers, 2005.

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Stützle, Thomas, Mauro Birattari i Holger H. Hoos, red. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74446-7.

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Stützle, Thomas, Mauro Birattari i Holger H. Hoos, red. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03751-1.

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Thomas, Stützle, Birattari Mauro i Hoos Holger H, red. Engineering stochastic local search algorithms: Designing, implementing and analyzing effective heuristics : international workshop, SLS 2007, Brussels, Belgium, September 6-8, 2007 : proceedings. Berlin: Springer, 2007.

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H, Hoos Holger, Birattari Mauro i SpringerLink (Online service), red. Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics: Second International Workshop, SLS 2009, Brussels, Belgium, September 3-4, 2009. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Stochastic Local Search. Elsevier, 2005. http://dx.doi.org/10.1016/b978-1-55860-872-6.x5016-1.

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Hoos, Holger H. Stochastic Local Search - Methods, Models, Applications. Ios Pr Inc, 1999.

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Hoos, Holger H., i Thomas Stützle. Stochastic Local Search: Foundations and Applications. Elsevier Science & Technology Books, 2004.

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Abed, Sa'Ed. Efficient Implementation of Parallel SAT Solver Based Stochastic Local Search. Nova Science Publishers, Incorporated, 2020.

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Części książek na temat "Stochastic local search"

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Bhatnagar, S., H. Prasad i L. Prashanth. "Deterministic Algorithms for Local Search". W Stochastic Recursive Algorithms for Optimization, 13–15. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4285-0_2.

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Hoos, Holger H., i Thomas Stützle. "Stochastic Local Search Algorithms: An Overview". W Springer Handbook of Computational Intelligence, 1085–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43505-2_54.

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de Lima Martins, Simone, Isabel Rosseti i Alexandre Plastino. "Data Mining in Stochastic Local Search". W Handbook of Heuristics, 39–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-07124-4_11.

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Martins, Simone de Lima, Isabel Rosseti i Alexandre Plastino. "Data Mining in Stochastic Local Search". W Handbook of Heuristics, 1–49. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-07153-4_11-1.

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Di Gaspero, Luca, i Andrea Roli. "Flexible Stochastic Local Search for Haplotype Inference". W Lecture Notes in Computer Science, 74–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-11169-3_6.

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Yu, Tong, Branislav Kveton i Ole J. Mengshoel. "Thompson Sampling for Optimizing Stochastic Local Search". W Machine Learning and Knowledge Discovery in Databases, 493–510. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71249-9_30.

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Winfree, Erik. "Chemical Reaction Networks and Stochastic Local Search". W Lecture Notes in Computer Science, 1–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26807-7_1.

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Araujo, Janniele A. S., Haroldo G. Santos, Davi D. Baltar, Túlio A. M. Toffolo i Tony Wauters. "Neighborhood Composition Strategies in Stochastic Local Search". W Hybrid Metaheuristics, 118–30. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39636-1_9.

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Codognet, Philippe, i Daniel Diaz. "Yet Another Local Search Method for Constraint Solving". W Stochastic Algorithms: Foundations and Applications, 73–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45322-9_5.

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Arcuri, Andrea. "Theoretical Analysis of Local Search in Software Testing". W Stochastic Algorithms: Foundations and Applications, 156–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04944-6_13.

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Streszczenia konferencji na temat "Stochastic local search"

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Lasisi, Ramoni O., i Robert DuPont. "Augmenting Stochastic Local Search with Heuristics". W 2018 9th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON). IEEE, 2018. http://dx.doi.org/10.1109/uemcon.2018.8796721.

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Pošik, Petr. "Stochastic local search in continuous domains". W the 12th annual conference comp. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1830761.1830830.

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Achlioptas, Dimitris, i Fotis Iliopoulos. "Focused Stochastic Local Search and the Lovász Local Lemma". W Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974331.ch141.

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Santiago, Rafael, i Luís C. Lamb. "Efficient Stochastic Local Search for Modularity Maximization". W GECCO '16: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2908961.2909003.

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Hossain, Muktadir, Tajkia Tasnim, Swakkhar Shatabda i Dewan M. Farid. "Stochastic local search for pattern set mining". W 2014 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA). IEEE, 2014. http://dx.doi.org/10.1109/skima.2014.7083547.

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Mengshoel, Ole Jakob, Tong Yu, Jon Riege i Eirik Flogard. "Stochastic local search for efficient hybrid feature selection". W GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3459438.

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Bahamida, Bachir, i Dalila Boughaci. "Intrusion Detection Using Fuzzy Stochastic Local Search Classifier". W 2012 11th Mexican International Conference on Artificial Intelligence (MICAI). IEEE, 2012. http://dx.doi.org/10.1109/micai.2012.17.

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van der Lee, Tim, Georgios Exarchakos i Sonia Heemstra de Groot. "Distributed Wireless Network Optimization With Stochastic Local Search". W 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC). IEEE, 2020. http://dx.doi.org/10.1109/ccnc46108.2020.9045189.

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Dong, Sheqin, Fan Guo, Jun Yuan, Rensheng Wang i Xianlong Hong. "Stochastic Local Search Using the Search Space Smoothing Meta-Heuristic:A Case Study". W 9th Joint Conference on Information Sciences. Paris, France: Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.213.

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Brunato, Mauro, i Roberto Battiti. "Stochastic Local Search for direct training of threshold networks". W 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280770.

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