Results 21 to 30 of about 925,172 (288)
Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions
Journal of Mathematical Sciences and Modelling, 2022 Meta-heuristic optimization algorithms are used in many application areas to solve optimization problems. In recent years, meta-heuristic optimization algorithms have gained importance over deterministic search algorithms in solving optimization problems.
Mahmut Dirik
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CherryChèvre: A fine-grained dataset for goat detection in natural environments
Scientific Data, 2023 We introduce a new dataset for goat detection that contains 6160 annotated images captured under varying environmental conditions. The dataset is intended for developing machine learning algorithms for goat detection, with applications in precision ...
Jehan-Antoine Vayssade +3 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Land surface temperature (LST) is one of important parameter in the earth energy and water budget. Recently, open-source modules or packages are emerging as all-in-one solutions to speed up the rapid development and critical application of LST from ...
Jinshun Zhu +6 more
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New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
Algorithms, 2018 The Map-Reduce (MR) framework has become a popular framework for developing new parallel algorithms for Big Data. Efficient algorithms for data mining of big data and distributed databases has become an important problem.
Yaron Gonen, Ehud Gudes, Kirill Kandalov
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Reference-Based Sequence Classification
IEEE Access, 2020 Sequence classification is an important data mining task in many real-world applications. Over the past few decades, many sequence classification methods have been proposed from different aspects.
Zengyou He +4 more
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Towards large scale continuous EDA: a random matrix theory perspective [PDF]
, 2013 Estimation of distribution algorithms (EDA) are a major branch of evolutionary algorithms (EA) with some unique advantages in principle. They are able to take advantage of correlation structure to drive the search more efficiently, and they are able to ...
Bootkrajang, Jakramate +2 more
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ADVANCE OF SMARANDACHE APPROACH TO SOLVING SYSTEMS OF DIOPHANTlNE EQUATIONS [PDF]
, 2003 By developing F. Smarandache (algebraic) approach to solving systems of Diophantine equations we elaborate a set of new computative algorithms and analytical formulae, which may be used for fmding numerical solutions of some combinatorial and number ...
CHEBRAKOV, Y.V.
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Nature-Inspired Algorithms in Real-World Optimization Problems
Mendel, 2017 Eight popular nature inspired algorithms are compared with the blind random search and three advanced adaptive variants of differential evolution (DE) on real-world problems benchmark collected for CEC 2011 algorithms competition.
Petr Bujok, Josef Tvrdik, Radka Polakova
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Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
Applied Sciences, 2021 In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in ...
Fernando Peres, Mauro Castelli
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AUTOMATIC ALGORITHM DEVELOPMENT USING NEW REINFORCEMENT PROGRAMMING TECHNIQUES [PDF]
Computational Intelligence, 2012 Reinforcement Programming (RP) is a new approach to automatically generating algorithms that uses reinforcement learning techniques. This paper introduces the RP approach and demonstrates its use to generate a generalized, in‐place, iterative sort algorithm. The RP approach improves on earlier results that use genetic programming (GP).
Spencer White +2 more
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