Application of Genetic Algorithms to Optimize a Truncated Mean k-Nearest Neighbours Regressor for Hotel Reservation Forecasting

2012 ◽  
pp. 79-90
Author(s):  
Andrés Sanz-García ◽  
Julio Fernández-Ceniceros ◽  
Fernando Antoñanzas-Torres ◽  
F. Javier Martínez-de-Pisón-Ascacibar
Keyword(s):  
Genetic Algorithms ◽  
Hotel Reservation ◽  
Nearest Neighbours

scholarly journals Shrinking Bouma’s window: How to model crowding in dense displays

2021 ◽  
Vol 17 (7) ◽  
pp. e1009187
Author(s):  
Alban Bornet ◽  
Adrien Doerig ◽  
Michael H. Herzog ◽  
Gregory Francis ◽  
Erik Van der Burg
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Human Performance ◽  
Human Behaviour ◽  
Human Vision ◽  
Nearest Neighbours ◽  
Visual Crowding

In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma’s law, i.e., all elements within a certain distance interfere with the target, and that adding more elements always leads to stronger crowding. Crowding is predominantly studied using sparse displays (a target surrounded by a few flankers). However, many studies have shown that this approach leads to wrong conclusions about human vision. Van der Burg and colleagues proposed a paradigm to measure crowding in dense displays using genetic algorithms. Displays were selected and combined over several generations to maximize human performance. In contrast to Bouma’s law, only the target’s nearest neighbours affected performance. Here, we tested various models to explain these results. We used the same genetic algorithm, but instead of selecting displays based on human performance we selected displays based on the model’s outputs. We found that all models based on the traditional feedforward pooling framework of vision were unable to reproduce human behaviour. In contrast, all models involving a dedicated grouping stage explained the results successfully. We show how traditional models can be improved by adding a grouping stage.

MEANS SQUARE DISPLACEMENTS OF A SUBSTITUTIONAL DEFECT AND ITS NEAREST NEIGHBOURS IN KI

1981 ◽  
Vol 42 (C6) ◽  
pp. C6-899-C6-901
Author(s):  
S. Duraiswamy ◽  
T. M. Haridasan
Keyword(s):  
Substitutional Defect ◽  
Nearest Neighbours

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

Optimizing Time-multiplexing Raster Cellular Neural Network simulator using genetic algorithms with RK8(6)

2011 ◽  
Vol 3 (6) ◽  
pp. 87-90
Author(s):  
O. H. Abdelwahed O. H. Abdelwahed ◽  
◽  
M. El-Sayed Wahed ◽  
O. Mohamed Eldaken
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Cellular Neural Network ◽  
Network Simulator

Optimization of Linear Equations using Genetic Algorithms

2011 ◽  
Vol 2 (3) ◽  
pp. 56-58
Author(s):  
Roshni .V Patel ◽  
◽  
Jignesh. S Patel
Keyword(s):  
Genetic Algorithms ◽  
Linear Equations
Sign in / Sign up

Export Citation Format

Share Document