Search results for " search"

Multilayer neural networks: an experimental evaluation of on-line training methods

2004

Artificial neural networks (ANN) are inspired by the structure of biological neural networks and their ability to integrate knowledge and learning. In ANN training, the objective is to minimize the error over the training set. The most popular method for training these networks is back propagation, a gradient descent technique. Other non-linear optimization methods such as conjugate directions set or conjugate gradient have also been used for this purpose. Recently, metaheuristics such as simulated annealing, genetic algorithms or tabu search have been also adapted to this context.There are situations in which the necessary training data are being generated in real time and, an extensive tr…

Blood Component Therapy and Coagulopathy in Trauma: A Systematic Review of the Literature from the Trauma Update Group

2016

Background Traumatic coagulopathy is thought to increase mortality and its treatment to reduce preventable deaths. However, there is still uncertainty in this field, and available literature results may have been overestimated. Methods We searched the MEDLINE database using the PubMed platform. We formulated four queries investigating the prognostic weight of traumatic coagulopathy defined according to conventional laboratory testing, and the effectiveness in reducing mortality of three different treatments aimed at contrasting coagulopathy (high fresh frozen plasma/packed red blood cells ratios, fibrinogen, and tranexamic acid administration). Randomized controlled trials were selected alo…

Efficient GRASP+VND and GRASP+VNS metaheuristics for the traveling repairman problem

2011

Abstract: The traveling repairman problem is a customer-centric routing problem, in which the total waiting time of the customers is minimized, rather than the total travel time of a vehicle. To date, research on this problem has focused on exact algorithms and approximation methods. This paper presents the first metaheuristic approach for the traveling repairman problem.

Fuzzified Tree Search in Real Domain Games

2011

Fuzzified game tree search algorithm is based on the idea that the exact game tree evaluation is not required to find the best move. Therefore, pruning techniques may be applied earlier resulting in faster search and greater performance. Applied to an abstract domain, it outperforms the existing ones such as Alpha-Beta, PVS, Negascout, NegaC*, SSS*/ Dual* and MTD(f). In this paper we present experimental results in real domain games, where the proposed algorithm demonstrated 10 percent performance increase over the existing algorithms.

AIs for Dominion Using Monte-Carlo Tree Search

2015

Dominion is a complex game, with hidden information and stochastic elements. This makes creating any artificial intelligence AI challenging. To this date, there is little work in the literature on AI for Dominion, and existing solutions rely upon carefully tuned finite-state solutions. This paper presents two novel AIs for Dominion based on Monte-Carlo Tree Search MCTS methods. This is achieved by employing Upper Confidence Bounds UCB and Upper Confidence Bounds applied to Trees UCT. The proposed solutions are notably better than existing work. The strongest proposal is able to win 67% of games played against a known, good finite-state solution, even when the finite-state solution has the u…

Right-arm rotation distance between binary trees

2003

We consider a transformation on binary trees, named right-arm rotation, which is a special instance of the well-known rotation transformation. Only rotations at nodes of the right arm of the trees are allowed. Using ordinal tools, we give an efficient algorithm for computing the right-arm rotation distance between two binary trees, i.e., the minimum number of rightarm rotations necessary to transform one tree into the other.

Short notes: Some Properties of the Rotation Lattice of Binary Trees

1988

Determinants and effects of external knowledge search: Focusing on organizational formal structure

2013

Tesis Doctoral presentada por Ana García Granero en el Departament de Direcció d'Empreses "Juan José Renau Piqueras" de la Universitat de València - Facultat d'Economia y realizada en el Instituto de Gestión de la Innovación y del Conocimiento (INGENIO, CSIC-UPV).

Forward Kinematic Modelling with Radial Basis Function Neural Network Tuned with a Novel Meta-Heuristic Algorithm for Robotic Manipulators

2022

The complexity of forward kinematic modelling increases with the increase in the degrees of freedom for a manipulator. To reduce the computational weight and time lag for desired output transformation, this paper proposes a forward kinematic model mapped with the help of the Radial Basis Function Neural Network (RBFNN) architecture tuned by a novel meta-heuristic algorithm, namely, the Cooperative Search Optimisation Algorithm (CSOA). The architecture presented is able to automatically learn the kinematic properties of the manipulator. Learning is accomplished iteratively based only on the observation of the input–output relationship. Related simulations are carried out on a 3-Degrees…

Meklētājprogrammas Google Search ranžēšanas ietekmējošie SEO faktori un tīmekļa vietnes optimizācijas pasākumi

2020

. Diplomdarba mērķis ir noteikt risinājumus un piedāvāt SEO pasākumus tīmekļa vietnes tehniskajai SEO optimizācijai, kura ietekmē pozīciju interneta meklētājprogrammā Google Search. Piedāvātajiem SEO pasākumiem ir jābūt efektīviem, jo tie var praktiski mainīt meklētājprogrammā Google Search iegūto rezultātu pozīciju. Par galveno uzdevumu analītiskajā daļā tiek uzskatīts būtiskāko ietekmējošo SEO faktoru izvēle un paskaidrojumi, kādā veidā tie ietekmē pozīciju piešķiršanu Google Search. Savukārt projekta daļā tīmekļa vietnes tiek novērtētas pēc konkrētajiem SEO faktoriem, un otrajai tīmekļa vietnei tiek identificēti nepieciešamie SEO pasākumi, kuri pozitīvi ietekmēs pozīcijas rezultātus Goog…