Bibliografie tematiche / Hill climbing

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Letteratura scientifica selezionata sul tema "Hill climbing"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 23 febbraio 2023

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Articoli di riviste sul tema "Hill climbing"

Pendergrass, L. "Climbing the commercialization hill". IEEE Instrumentation and Measurement Magazine 9, n. 1 (febbraio 2006): 12–21. http://dx.doi.org/10.1109/mim.2006.1634952.

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Anam, Hairul, Feby Sabilhul Hanafi, Ahmad Fauzal Adifia, Ahmad Firdaus Ababil e Saiful Bukhori. "Penerapan Metode Steepest Ascent Hill Climb pada Permainan Puzzle". INFORMAL: Informatics Journal 3, n. 2 (31 agosto 2018): 36. http://dx.doi.org/10.19184/isj.v3i2.9987.

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Puzzle is one example of the application of artificial intelligence, in the process of completion there are many search algorithms that can be applied. The 8 puzzle solution will be faster obtained if the array principle is used with a variation of the Steepest-Ascent Hill Climbing (Hill Climbing algorithm by choosing the sharpest / steepest slope) with the correct heuristic parameters and distance heuristics and combined with LogList as the storage state ever passed to overcome the problems in the hill climbing algorithm itself and avoid the looping state that has been passed. Steepest Ascent Hill Climbing is an algorithm method that is widely used for optimization problems. The application of the SAHC (Steepest Ascent Hill Climbing) Algorithm to the puzzle is needed so that the game is completed with optimal time.

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MURAMATSU, Daigo. "Hill-Climbing Attacks and Robust Online Signature Verification Algorithm against Hill-Climbing Attacks". IEICE Transactions on Information and Systems E93-D, n. 3 (2010): 448–57. http://dx.doi.org/10.1587/transinf.e93.d.448.

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Abualigah, Laith Mohammad, Essam Said Hanandeh, Ahamad Tajudin Khader, Mohammed Abdallh Otair e Shishir Kumar Shandilya. "An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem". Current Medical Imaging Formerly Current Medical Imaging Reviews 16, n. 4 (7 maggio 2020): 296–306. http://dx.doi.org/10.2174/1573405614666180903112541.

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Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

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Ilwaru, Venn Y. I., Tesa Sumah, Yopi Andry Lesnussa e Zeth A. Leleury. "PERBANDINGAN ALGORITMA HILL CLIMBING DAN ALGORITMA ANT COLONY DALAM PENENTUAN RUTE OPTIMUM". BAREKENG: Jurnal Ilmu Matematika dan Terapan 11, n. 2 (1 dicembre 2017): 139–50. http://dx.doi.org/10.30598/barekengvol11iss2pp139-150.

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Optimasi adalah pencarian nilai-nilai variabel yang dianggap optimal untuk mencapai hasil yang diinginkan. Untuk memecahkan masalah optimasi tersebut, tentunya diperlukan algoritma yang handal. Algoritma Hill Climbing dan Algotrima Ant Colony adalah metode dari sekian banyak metode kecerdasan buatan untuk menyelesaikan permasalahan optimasi. Karena algoritmanya yang cukup sederhana, metode Hill Climbing telah banyak diterapkan dalam berbagai aplikasi. Disamping itu metode Hill Climbing juga mengefisienkan penggunaan memori yang besar. Algoritma Ant Colony adalah algoritma yang diadopsi dari perilaku koloni semut. Secara alamiah koloni semut mampu menemukan rute terpendek dalam perjalanan dari sarang ke tempat-tempat sumber makanan, berdasarkan jejak kaki pada lintasan yang telah dilewati. Dari hasil penelitian yang dilakukan dengan menggunakan algoritma Hill Climbing dan Algoritma Ant Colony diperoleh rute optimum ferry di Pulau Ambon, Pulau Seram, dan Pulau-Pulau Lease yang berbeda. Pada Algoritma Hill Climbing diperoleh rute yang optimal yaitu Tulehu – Wainama – Umeputih – Wailey – Amahai - Nalahia dengan jarak tempuh 126 Km, sedangkan menggunakan Algoritma Ant Colony diperoleh rute yang optimal yaitu Tulehu – Wainama – Umeputih – Wailey – Amahai – Nalahia - Tulehu dengan jarak tempuh 197 Km

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Vaughan, Diane E., Sheldon H. Jacobson e Derek E. Armstrong. "A New Neighborhood Function for Discrete Manufacturing Process Design Optimization Using Generalized Hill Climbing Algorithms". Journal of Mechanical Design 122, n. 2 (1 marzo 2000): 164–71. http://dx.doi.org/10.1115/1.533566.

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Discrete manufacturing process design optimization can be difficult, due to the large number of manufacturing process design sequences and associated input parameter setting combinations that exist. Generalized hill climbing algorithms have been introduced to address such manufacturing design problems. Initial results with generalized hill climbing algorithms required the manufacturing process design sequence to be fixed, with the generalized hill climbing algorithm used to identify optimal input parameter settings. This paper introduces a new neighborhood function that allows generalized hill climbing algorithms to be used to also identify the optimal discrete manufacturing process design sequence among a set of valid design sequences. The neighborhood function uses a switch function for all the input parameters, hence allows the generalized hill climbing algorithm to simultaneously optimize over both the design sequences and the inputs parameters. Computational results are reported with an integrated blade rotor discrete manufacturing process design problem under study at the Materials Process Design Branch of the Air Force Research Laboratory, Wright Patterson Air Force Base (Dayton, Ohio, USA). [S1050-0472(00)01002-3]

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Basseur, Matthieu, Adrien Goëffon, Frédéric Lardeux, Frédéric Saubion e Vincent Vigneron. "On the Attainability of NK Landscapes Global Optima". Proceedings of the International Symposium on Combinatorial Search 5, n. 1 (1 settembre 2021): 28–34. http://dx.doi.org/10.1609/socs.v5i1.18312.

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In this paper, we aim at evaluating the impact of the starting point of a basic local search based on the first improvement strategy. We define the coverage rate of a configuration as the proportion of the search space from which a particular configuration can be reached by a strict hill-climbling with a non-zero probability. In particular, we compute the coverage rate of fitness landscapes global optima, in order to evaluate their attainability by hill-climbing algorithms. The experimental study is realized on NK landscapes, in which the size and ruggedness can be controlled. Results indicate that the coverage rate of global optima is usually high, which means that a basic strictly improving hill-climbing with first improvement strategy is able to reach global optima, independently to the starting point considered. This confirms that it is more important to focus on an effective search strategy rather than worrying about the choice of the initial configurations.

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Senese, Donald J. "Climbing the Electronic Capitol Hill". Serials Review 28, n. 4 (dicembre 2002): 287–94. http://dx.doi.org/10.1080/00987913.2002.10764761.

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Ervine, Alice. "Instructions for Climbing a Hill". Appalachian Heritage 17, n. 3 (1989): 38. http://dx.doi.org/10.1353/aph.1989.0136.

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Heap, Danny, Peter Danziger e Eric Mendelsohn. "Hill-climbing to Pasch valleys". Journal of Combinatorial Designs 15, n. 5 (2007): 405–19. http://dx.doi.org/10.1002/jcd.20142.

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Tesi sul tema "Hill climbing"

Choi, Seungryul. "Hill-climbing SMT processor resource distribution". College Park, Md. : University of Maryland, 2006. http://hdl.handle.net/1903/3514.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2006.
Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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Sullivan, Kelly Ann. "A Convergence Analysis of Generalized Hill Climbing Algorithms". Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/27027.

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Generalized hill climbing (GHC) algorithms provide a unifying framework for describing several discrete optimization problem local search heuristics, including simulated annealing and tabu search. A necessary and a sufficient convergence condition for GHC algorithms are presented. The convergence conditions presented in this dissertation are based upon a new iteration classification scheme for GHC algorithms. The convergence theory for particular formulations of GHC algorithms is presented and the implications discussed. Examples are provided to illustrate the relationship between the new convergence conditions and previously existing convergence conditions in the literature. The contributions of the necessary and the sufficient convergence conditions for GHC algorithms are discussed and future research endeavors are suggested.
Ph. D.

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Johnson, Alan W. "Generalized hill climbing algorithms for discrete optimization problems". Diss., This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-06062008-152638/.

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Vaughan, Diane Elizabeth. "Simultaneous Generalized Hill Climbing Algorithms for Addressing Sets of Discrete Optimization Problems". Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/28514.

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Generalized hill climbing (GHC) algorithms provide a framework for using local search algorithms to address intractable discrete optimization problems. Many well-known local search algorithms can be formulated as GHC algorithms, including simulated annealing, threshold accepting, Monte Carlo search, and pure local search (among others). This dissertation develops a mathematical framework for simultaneously addressing a set of related discrete optimization problems using GHC algorithms. The resulting algorithms, termed simultaneous generalized hill climbing (SGHC) algorithms, can be applied to a wide variety of sets of related discrete optimization problems. The SGHC algorithm probabilistically moves between these discrete optimization problems according to a problem generation probability function. This dissertation establishes that the problem generation probability function is a stochastic process that satisfies the Markov property. Therefore, given a SGHC algorithm, movement between these discrete optimization problems can be modeled as a Markov chain. Sufficient conditions that guarantee that this Markov chain has a uniform stationary probability distribution are presented. Moreover, sufficient conditions are obtained that guarantee that a SGHC algorithm will visit the globally optimal solution over all the problems in a set of related discrete optimization problems. Computational results are presented with SGHC algorithms for a set of traveling salesman problems. For comparison purposes, GHC algorithms are also applied individually to each traveling salesman problem. These computational results suggest that optimal/near optimal solutions can often be reached more quickly using a SGHC algorithm.
Ph. D.

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Namazi, Majid. "Learning in Combinatorial Constraint Optimisation". Thesis, Griffith University, 2022. http://hdl.handle.net/10072/419082.

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Many real-world problems can be modelled as constraint optimisation problems (COPs). Each COP includes a set of variables with domains of values, constraints on the assignments to the variables, and an objective function, which should be minimised or maximised. In this thesis, we consider only combinatorial COPs, where domains of the variables are discrete. A component is a subproblem of a COP with specific variables, assignable values or constraints. Most practical COPs, including waste collection, mail delivery, supply chain management, and travelling thief problem (TTP), have more than one component. Existing methods for solving COPs, especially multi-component COPs, repeatedly solve the same problem or subproblem but do not take advantage of learning during the search. This research aimed to apply memorising and online or adaptive machine learning models. The memory buffers and the ML models are built, deployed, and updated during the search to improve search efficacy and efficiency in solving COPs, especially multi-component ones. In this research, we have developed a history memorising method to enhance diversity and effectiveness in solving COPs. Also, we have developed three online machine learning-based methods, one coordination learning for improving efficacy and two surrogate models for enhancing the efficiency of TTP solving. Our proposed solver, CoCo, is currently the state-of-the-art solver for solving TTP. History memorising is an online low-level learning method to keep previously visited solutions or their objective values to avoid or escape from local optima during the search. The Late Acceptance Hill Climbing (LAHC) is a history memorising metaheuristic with promising performance on some COP domains. It aims to overcome the main downside of the traditional Hill Climbing (HC) search, which is often quickly trapped in a local optimum due to strictly accepting only non-worsening moves within each iteration. In contrast, LAHC also accepts worsening moves by keeping the objective values of the previously visited solutions in a limited-size circular memory. It compares the fitness values of candidate solutions against the least recent element in the circular memory to decide on accepting or rejecting them. However, we have realised that whenever all values in memory become the same, LAHC behaves like HC and gets stuck in local optima. We propose an improved form of LAHC called Diversified Late Acceptance Search (DLAS) for solving COPs in general, which usually uses much smaller memory, converges much faster than LAHC and escapes local optima much better than LAHC. The proposed DLAS approach outperforms LAHC on benchmark sets of Travelling Salesman Problem (TSP) and Quadratic Assignment Problem (QAP) instances. TTP is an academic proxy for the waste collection and mail delivery real-world optimisation problems composed of TSP and Knapsack Problem (KP). In TTP, a thief makes a cyclic tour through a set of cities while collecting profitable items scattered over the cities into a rented capacitated knapsack. As the weight of the knapsack increases, the thief’s speed decreases; hence the renting cost increases. Solving TTP aims to maximise profit while minimising the renting cost simultaneously, which means maximising the difference between profit and renting cost. Existing TTP solvers typically employ interleaving and solve one component at a time while keeping the solution of the other component unchanged. This form of interleaving essentially means poor coordination in solving TTP. In this thesis, we first show that a simple local search based coordination approach does not work in TTP. Then, to adequately address interdependence between TSP and KP components, we propose a human-designed coordination heuristic that adjusts collection plans during the exploration of cyclic tours. We further propose another human-designed coordination heuristic that explicitly exploits the cyclic tours in selecting items during collection plan exploration. Lastly, we propose an online machine learning-based coordination heuristic that captures the characteristics of the two human-designed coordination heuristics while solving any TTP instance. Our proposed coordination-based approaches help our TTP solver, cooperative coordination (CoCo), significantly outperform existing state-of-the-art TTP solvers on a set of benchmark TTP instances. Our proposed CoCo solver modifies a TTP instance’s underlying TSP and KP solutions in an iterative interleaved fashion. The TSP solution as a cyclic tour is typically changed in a deterministic way using the steepest-ascent Hill-Climbing (HC) search similar to other cooperative solvers. In contrast, changes to the KP solution typically involve a random HC search, effectively resulting in a quasi-meandering exploration of the TTP solution space. Once CoCo reaches a plateau, it restarts the iterative search of the TTP solution space by using a new initial cyclic tour. We have noticed that the final objective value remains almost the same if the same or similar initial cyclic tour is tried several times by CoCo or the other cooperative TTP solvers. Considering this semideterministic nature of the state-of-the-art cooperative TTP solvers, we propose two adaptive and online surrogate models to filter out non-promising initial cyclic tours to improve search efficiency. These surrogate models are automatically built, updated and deployed while solving any TTP instance. The first model is a Support Vector Regression (SVR)-based black-box model, and the second is a K Nearest Neighbour (KNN)-based white-box simulation model. Both models help to filter out non-promising initial cyclic tours while losing a small number of the cyclic tours leading to the best overall solutions. However, the KNN-based white-box simulation model is more accurate and efficient.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Venkatraman, Chandrasekar. "Hill climbing digital control algorithm for maximum power point tracking of photovoltaic arrays". Laramie, Wyo. : University of Wyoming, 2006. http://proquest.umi.com/pqdweb?did=1320938081&sid=2&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Mahdavi, Kiarash. "A clustering genetic algorithm for software modularisation with a multiple hill climbing approach". Thesis, Brunel University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.425197.

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Silva, Arthur de Assis. "Um algoritmo baseado na metaheurística late acceptance hill-climbing para o planejamento operacional de lavra". reponame:Repositório Institucional da UFOP, 2014. http://www.repositorio.ufop.br/handle/123456789/3733.

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Programa de Pós-Graduação em Ciência da Computação. Departamento de Ciência da Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto.
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Este trabalho trata um problema particular de planejamento de lavra de uma mineradora localizada no quadrilátero ferrífero do Estado de Minas Gerais, Brasil. Neste problema há um conjunto de frentes de lavra, um conjunto de equipamentos de carga de diferentes produtividades, um conjunto de caminhões de diferentes capacidades e um conjunto de pontos de descarga para o material lavrado. Cada frente de lavra é subdividida em blocos, os quais, por sua vez, são subdivididos em sub-blocos. Cada sub-bloco pode conter um dentre quatro tipos de material: hematita, canga, itabirito e estéril. Além disso, cada sub-bloco somente pode ser lavrado se os sub-blocos precedentes tiverem sido totalmente lavrados. A cada ponto de descarga está associada uma meta de produção e uma qualidade de material a ser atendida. O objetivo é determinar a alocação das carregadeiras aos blocos e o número de viagens que cada caminhão deve fazer a cada sub-bloco, saindo de um determinado ponto de descarga, de forma a atender as metas de produção e qualidade estabelecidas para cada descarga. Para resolvê-lo foi desenvolvido um algoritmo heurístico baseado nas metaheurísticas Greedy Randomized Adaptive Search Procedures (GRASP) e Late Acceptance Hill-Climbing (LAHC). O algoritmo explora o espaço de soluções usando busca locais autoadaptativas. Experimentos computacionais comparam os resultados do algoritmo proposto com aqueles do otimizador LINGO aplicado a um modelo de programação linear inteira mista e mostram a efetividade da proposta. ________________________________________________________________________________________________
ABSTRACT: This work deals with a particular problem of mine planning at a mining company located in the Iron Quadrangle of Minas Gerais, Brazil. In this problem there is a set of pit mining, a set of loader equipment of different yields, a set of trucks of different capacities and a set of delivery points for the discharge of materials. Each pit is subdivided into blocks, which, in turn, are subdivided into sub-blocks. Each sub-block can contain one of four types of material: hematite, canga, itabirito and waste. Furthermore, each sub-block can only be drawn up if the preceding sub-blocks have been fully drawn up. Every point of discharge is associated with a production and quality targets of material to be answered. The objective is to determine the allocation of loaders to blocks and the number of trips that each truck must do for each sub-block, leaving a certain point of discharge in order to meet production and quality targets requirements for each discharge. A heuristic algorithm, based on the metaheuristics Greedy Randomized Adaptive Search Procedures and Late Acceptance Hill-Climbing, was developed in order to solve this problem. The algorithm explores the solution space using self-adaptive local search. Computational experiments compare the results of the proposed algorithm with those of the optimizer LINGO model applied to a mixed integer linear programming and show its effectiveness.

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Hassan, Fadratul Hafinaz. "Heuristic search methods and cellular automata modelling for layout design". Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7581.

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Spatial layout design must consider not only ease of movement for pedestrians under normal conditions, but also their safety in panic situations, such as an emergency evacuation in a theatre, stadium or hospital. Using pedestrian simulation statistics, the movement of crowds can be used to study the consequences of different spatial layouts. Previous works either create an optimal spatial arrangement or an optimal pedestrian circulation. They do not automatically optimise both problems simultaneously. Thus, the idea behind the research in this thesis is to achieve a vital architectural design goal by automatically producing an optimal spatial layout that will enable smooth pedestrian flow. The automated process developed here allows the rapid identification of layouts for large, complex, spatial layout problems. This is achieved by using Cellular Automata (CA) to model pedestrian simulation so that pedestrian flow can be explored at a microscopic level and designing a fitness function for heuristic search that maximises these pedestrian flow statistics in the CA simulation. An analysis of pedestrian flow statistics generated from feasible novel design solutions generated using the heuristic search techniques (hill climbing, simulated annealing and genetic algorithm style operators) is conducted. The statistics that are obtained from the pedestrian simulation is used to measure and analyse pedestrian flow behaviour. The analysis from the statistical results also provides the indication of the quality of the spatial layout design generated. The technique has shown promising results in finding acceptable solutions to this problem when incorporated with the pedestrian simulator when demonstrated on simulated and real-world layouts with real pedestrian data.

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Dorfmüller, Gabi. "Eine relationale Strategie zur Einteilung von Gruppen auf Basis flüchtiger Kontakte". [S.l. : s.n.], 2005. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11729995.

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Libri sul tema "Hill climbing"

Glucklich, Ariel. Climbing Chamundi Hill. New York: HarperCollins, 2007.

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Romeo, Fabio I. Probabilistic hill climbing algorithms: Properties and applications. Berkeley, CA: University of California, 1986.

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Foerstel, Karen. Climbing the Hill: Gender conflict in Congress. Westport, CT: Praeger, 1996.

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Glucklich, Ariel. Climbing Chamundi Hill: 1001 steps with a storyteller and a reluctant pilgrim. San Francisco: HarperSanFrancisco, 2003.

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Summerson, John. The complete guide to climbing (by bike): A guide to cycling climbing and the most difficult hill climbs in the United States. Winston-Salem, NC: Extreme Press, 2007.

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Mountaineering: The freedom of the hills. 8^a ed. Seattle: Mountaineers Books, 2010.

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Pelt, Julie Van. Mountaineering: The freedom of the hills. 8^a ed. Seattle: Mountaineers Books, 2010.

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1951-, Cox Steven M., Fulsaas Kris e Mountaineers (Society), a cura di. Mountaineering: The freedom of the hills. 7^a ed. Seattle, WA: Mountaineers Books, 2003.

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Don, Graydon, e Mountaineers (Society), a cura di. Mountaineering: The freedom of the hills. 5^a ed. Seattle: Mountaineers, 1992.

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Eng, Ronald C. Mountaineering: The freedom of the hills. 8^a ed. Seattle: Mountaineers Books, 2010.

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Capitoli di libri sul tema "Hill climbing"

Borghoff, Julia, Lars R. Knudsen e Krystian Matusiewicz. "Hill Climbing Algorithms and Trivium". In Selected Areas in Cryptography, 57–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19574-7_4.

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Cruz, Ricardo, Joaquim F. Pinto Costa e Jaime S. Cardoso. "Automatic Augmentation by Hill Climbing". In Lecture Notes in Computer Science, 115–24. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30484-3_10.

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Ganapathy, Ganeshkumar, Vijaya Ramachandran e Tandy Warnow. "Better Hill-Climbing Searches for Parsimony". In Lecture Notes in Computer Science, 245–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39763-2_19.

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Zeng, Sanyou, Hui Shi, Lishan Kang e Lixin Ding. "Orthogonal Dynamic Hill Climbing Algorithm: ODHC". In Studies in Computational Intelligence, 79–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-49774-5_4.

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Bahuguna, Saniya, e Ashok Pal. "β-Hill Climbing Grey Wolf Optimizer". In Advances in Intelligent Systems and Computing, 311–20. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2712-5_26.

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Iba, Hitoshi, Hugo de Garis e Taisuke Sato. "Genetic Programming with local hill-climbing". In Parallel Problem Solving from Nature — PPSN III, 302–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58484-6_274.

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Arriaga, Jonathan, e Manuel Valenzuela-Rendón. "Steepest Ascent Hill Climbing for Portfolio Selection". In Applications of Evolutionary Computation, 145–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29178-4_15.

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Basseur, Matthieu, e Adrien Goëffon. "Hill-Climbing Behavior on Quantized NK-Landscapes". In Lecture Notes in Computer Science, 209–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-44973-4_22.

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Davies, Krispin A., Alejandro Ramirez-Serrano, Graeme N. Wilson e Mahmoud Mustafa. "Rapid Control Selection through Hill-Climbing Methods". In Intelligent Robotics and Applications, 561–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33515-0_55.

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Millan, William, Andrew Clark e Ed Dawson. "Boolean Function Design Using Hill Climbing Methods". In Information Security and Privacy, 1–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48970-3_1.

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Atti di convegni sul tema "Hill climbing"

Khasawneh, Mohammad, e Patrick H. Madden. "Hill Climbing with Trees". In ISPD '20: International Symposium on Physical Design. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3372780.3375563.

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Hwang, Chein-Shung, e Show-Fen Lin. "Hill Climbing for Diversity Retrieval". In 2009 WRI World Congress on Computer Science and Information Engineering. IEEE, 2009. http://dx.doi.org/10.1109/csie.2009.624.

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Khor, Susan. "Hill climbing on discrete HIFF". In the 9th annual conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1277014.

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Basseur, Matthieu, e Adrien Goeffon. "Hill-climbing strategies on various landscapes". In Proceeding of the fifteenth annual conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2463372.2463439.

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Bazargani, Mosab, e Fernando G. Lobo. "Parameter-less late acceptance hill-climbing". In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3071178.3071225.

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Clay, Sylvain, Lucien Mousin, Nadarajen Veerapen e Laetitia Jourdan. "CLAHC - custom late acceptance hill climbing". In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3463129.

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Dunlap, Damion, Wei Yu, Emmanuel G. Collins e Charmane V. Caldwell. "Motion planning for steep hill climbing". In 2011 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2011. http://dx.doi.org/10.1109/icra.2011.5979709.

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Liu, Jialin, Diego Perez-Liebana e Simon M. Lucas. "Bandit-based Random Mutation Hill-Climbing". In 2017 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2017. http://dx.doi.org/10.1109/cec.2017.7969564.

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Chatterjee, Sabyasachi, e Prabir Banerjee. "Hill-Climbing approach for optimizing receiver bandwidth". In 2014 International Conference on Electronics, Communication and Instrumentation (ICECI). IEEE, 2014. http://dx.doi.org/10.1109/iceci.2014.6767366.

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Al-Betar, Mohammed Azmi, Mohammed A. Awadallah, Asaju La'aro Bolaji e Basem O. Alijla. "β-Hill Climbing Algorithm for Sudoku Game". In 2017 Palestinian International Conference on Information and Communication Technology (PICICT). IEEE, 2017. http://dx.doi.org/10.1109/picict.2017.11.

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Rapporti di organizzazioni sul tema "Hill climbing"

Imura, Shinya, Takehiko Kowatari, Hisaya Shimizu, Norikazu Matsuzaki, Daisuke Yamamoto e Yoshinori Ikeda. Hill-Climbing Performance of Batteryless Motorized-Four-Wheel-Drive System. Warrendale, PA: SAE International, maggio 2005. http://dx.doi.org/10.4271/2005-08-0395.

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Jacobson, Sheldon H. Designing Optimal Generalized Hill Climbing Algorithms with Applications to Discrete Manufacturing Process Design Optimization. Fort Belvoir, VA: Defense Technical Information Center, novembre 2003. http://dx.doi.org/10.21236/ada419522.

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Miles, Gaines E., Yael Edan, F. Tom Turpin, Avshalom Grinstein, Thomas N. Jordan, Amots Hetzroni, Stephen C. Weller, Marvin M. Schreiber e Okan K. Ersoy. Expert Sensor for Site Specification Application of Agricultural Chemicals. United States Department of Agriculture, agosto 1995. http://dx.doi.org/10.32747/1995.7570567.bard.

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Abstract (sommario):

In this work multispectral reflectance images are used in conjunction with a neural network classifier for the purpose of detecting and classifying weeds under real field conditions. Multispectral reflectance images which contained different combinations of weeds and crops were taken under actual field conditions. This multispectral reflectance information was used to develop algorithms that could segment the plants from the background as well as classify them into weeds or crops. In order to segment the plants from the background the multispectrial reflectance of plants and background were studied and a relationship was derived. It was found that using a ratio of two wavelenght reflectance images (750nm and 670nm) it was possible to segment the plants from the background. Once ths was accomplished it was then possible to classify the segmented images into weed or crop by use of the neural network. The neural network developed for this work is a modification of the standard learning vector quantization algorithm. This neural network was modified by replacing the time-varying adaptation gain with a constant adaptation gain and a binary reinforcement function. This improved accuracy and training time as well as introducing several new properties such as hill climbing and momentum addition. The network was trained and tested with different wavelength combinations in order to find the best results. Finally, the results of the classifier were evaluated using a pixel based method and a block based method. In the pixel based method every single pixel is evaluated to test whether it was classified correctly or not and the best weed classification results were 81% and its associated crop classification accuracy is 57%. In the block based classification method, the image was divided into blocks and each block was evaluated to determine whether they contained weeds or not. Different block sizes and thesholds were tested. The best results for this method were 97% for a block size of 8 inches and a pixel threshold of 60. A simulation model was developed to 1) quantify the effectiveness of a site-specific sprayer, 2) evaluate influence of diffeent design parameters on efficiency of the site-specific sprayer. In each iteration of this model, infected areas (weed patches) in the field were randomly generated and the amount of required herbicides for spraying these areas were calculated. The effectiveness of the sprayer was estimated for different stain sizes, nozzle types (conic and flat), nozzle sizes and stain detection levels of the identification system. Simulation results indicated that the flat nozzle is much more effective as compared to the conic nozzle and its relative efficiency is greater for small nozzle sizes. By using a site-specific sprayer, the average ratio between the spraying areas and the stain areas is about 1.1 to 1.8 which can save up to 92% of herbicides, especially when the proportion of the stain areas is small.

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