Two-dimensional phase unwrapping using a hybrid genetic algorithm

2007 ◽  
Vol 46 (5) ◽  
pp. 730 ◽  
Author(s):  
Salah A. Karout ◽  
Munther A. Gdeisat ◽  
David R. Burton ◽  
Michael J. Lalor
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Phase Unwrapping ◽  
Two Dimensional

Two-dimensional nesting system based on hybrid genetic algorithm

2009 ◽  
Vol 14 (1) ◽  
pp. 60-64
Author(s):  
Qingming Wu ◽  
Wei Yang ◽  
Qiang Zhang ◽  
Junjie Zhou
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Two Dimensional

A Hybrid Genetic Algorithm for Optimization of Two-Dimensional Cutting-Stock Problem

2012 ◽  
pp. 56-71
Author(s):  
Ahmed Mellouli ◽  
Faouzi Masmoudi ◽  
Imed Kacem ◽  
Mohamed Haddar
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Production Cost ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Genetic Approach ◽  
Cutting Pattern

In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfill the customer orders with minimal production cost. The effectiveness of the hybrid genetic approach has been evaluated through a set of instances which are both randomly generated and collected from the literature.

Multilevel redundancy allocation using two dimensional arrays encoding and hybrid genetic algorithm

2013 ◽  
Vol 64 (1) ◽  
pp. 69-83 ◽  
Author(s):  
Pan He ◽  
Kaigui Wu ◽  
Jie Xu ◽  
Junhao Wen ◽  
Zhuo Jiang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Two Dimensional ◽  
Redundancy Allocation

Scheduling Robotic Actions by Genetic Algorithms

1996 ◽  
Vol 5 (2) ◽  
pp. 191-204
Author(s):  
R. J. Abbott ◽  
M. L. Campbell ◽  
W. C. Krenz
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Real World ◽  
Hybrid Genetic Algorithm ◽  
Two Dimensional ◽  
The Real ◽  
Short Period

A hybrid genetic algorithm is used to schedule tasks for a satellite that can be modeled as a robot whose goal is to retrieve objects from a two-dimensional field. The objective is to find a schedule that maximizes the value of objects retrieved. Typical of the real-world tasks to which this corresponds is the scheduling of ground contacts for a communications satellite. An important feature of our application is that the amount of time available for running the scheduler is not necessarily known in advance. This requires that the scheduler produce reasonably good results after a short period, but that it also continue to improve its results if allowed to run for a longer period. We satisfy this requirement by developing what we call a sustainable genetic algorithm.

scholarly journals The Problem of Balancing an Inverted Spherical Pendulum on an Omniwheel Platform

2021 ◽  
Vol 17 (4) ◽  
pp. 507-525
Author(s):  
A. S. Shaura ◽  
◽  
V. A. Tenenev ◽  
E. V. Vetchanin ◽  
◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Inverted Pendulum ◽  
Initial Conditions ◽  
Equations Of Motion ◽  
Hybrid Genetic Algorithm ◽  
Three Dimensional ◽  
Two Dimensional ◽  
Spherical Pendulum ◽  
Pendulum System ◽  
End Point

This paper addresses the problem of balancing an inverted pendulum on an omnidirectional platform in a three-dimensional setting. Equations of motion of the platform – pendulum system in quasi-velocities are constructed. To solve the problem of balancing the pendulum by controlling the motion of the platform, a hybrid genetic algorithm is used. The behavior of the system is investigated under different initial conditions taking into account a necessary stop of the platform or the need for continuation of the motion at the end point of the trajectory. It is shown that the solution of the problem in a two-dimensional setting is a particular case of three-dimensional balancing.

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