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Fine-Tuning Temperatures in Restricted Boltzmann Machines Using Meta-Heuristic Optimization
(Ieee, 2020-01-01)
Restricted Boltzmann Machines (RBM) are stochastic neural networks mainly used for image reconstruction and unsupervised feature learning. An enhanced version, the temperature-based RBM (T-RBM), considers a new temperature ...
On the hypercomplex-based search spaces for optimization purposes
(2018-01-01)
Most applications can be modeled using real-valued algebra. Nevertheless, certain problems may be better addressed using different mathematical tools. In this context, complex numbers can be viewed as an alternative to ...
Exploring Selection and Generation Hyper-heuristic Approaches for the Job Shop Scheduling Problem
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-06)
The job-shop scheduling problem (JSSP) represents a challenging field of study with many industrial and real-world applications that skyrocket its importance. Solving a JSSP requires allocating a set of jobs in a set of ...
A heuristic approach to output-only system identification under transient excitation
(Pergamon-Elsevier Science Ltd, 2017-02)
Output-only system identification is a very attractive technique for its implementation simplicity. However, it requires long records to validate the white-noise assumption of the excitation, mainly under transient forced ...
Design of Sensor Networks for Chemical Plants Based on Meta-Heuristics
(Molecular Diversity Preservation International, 2009-02)
In this work the optimal design of sensor networks for chemical plants is addressed using stochastic optimization strategies. The problem consists in selecting the type, number and location of new sensors that provide the ...
An Evolutionary Framework for Producing Hyper-Heuristics for Solving the 2D Irregular Bin Packing Problem
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2015)
A heuristic search approach to planning with temporally extended preferences
(ELSEVIER SCIENCE BV, 2009)
Planning with preferences involves not only finding a plan that achieves the goal, it requires finding a preferred plan that achieves the goal, where preferences over plans are specified as part of the planner's input. In ...
DISEÑO PARAMÉTRICO DE MOTOR STIRLING
(2012-01-09)
The use of heuristics for solving optimization problems with restrictions is a useful
option, additional to classical techniques. Generally, heuristics are applied for the
search of solutions that can be considered as ...
Unsupervised land-cover classification through hyper-heuristic-based Harmony Search
(2015-11-10)
Unsupervised land-cover classification aims at learning intrinsic properties of spectral and spatial features for the task of area coverage in urban and rural areas. In this paper, we propose to model the problem of ...