Image clustering using genetic algorithm with tournament selection and uniform crossover

2020 ◽  
Vol 852 ◽  
pp. 012043
Author(s):  
Gevin Valerian ◽  
Tri Sutrisno ◽  
Dyah Erny Herwindiati
Keyword(s):  
Genetic Algorithm ◽  
Image Clustering ◽  
Uniform Crossover ◽  
Tournament Selection

scholarly journals Generalized solution for inverse kinematics problem of a robot using hybrid genetic algorithms

2018 ◽  
Vol 7 (4.6) ◽  
pp. 250
Author(s):  
Pavan Kumar K ◽  
Murali Mohan J ◽  
Srikanth D
Keyword(s):  
Genetic Algorithm ◽  
Inverse Kinematics ◽  
Dynamic Control ◽  
Hybrid Genetic Algorithm ◽  
Search Space ◽  
Generalized Solution ◽  
Inverse Kinematics Problem ◽  
Tournament Selection ◽  
Selection Operator ◽  
Forward Kinematic

The robot control consists of kinematic control and dynamic control. Control methods of the robot involve forward kinematics and inverse kinematics (IK). In Inverse kinematics the joint angles are found for a given position and orientation of the end effector. Inverse kinematics is a nonlinear problem and has multiple solutions. This computation is required to control the robot arms. A Genetic Algorithm (GA) and Hybrid genetic algorithm (HGA) (Genetic Algorithm in conjunction with Nelder-Mead technique) are proposed for solving the inverse kinematics of a robotic arm. HGA introduces two concepts exploration, exploitation. In an exploration phase, the GA identifies the good areas in entire search space and then exploitation phase is performed inside these areas by using Nelder- mead technique Binary Simulated Crossover and niching strategy for binary tournament selection operator is used. Proposed algorithms can be used on any type of manipulator and the only requirement is the forward kinematic equations, which are easily obtained. As a case study inverse kinematics of a Two Link Elbow Manipulator and PUMA manipulator are solved using GA and HGA in MATLAB. The algorithm is able to find all solutions without any error  

A GENETIC ALGORITHM BASED ON SEXUAL SELECTION FOR THE MULTIDIMENSIONAL 0/1 KNAPSACK PROBLEMS

2012 ◽  
Vol 09 ◽  
pp. 422-431 ◽  
Author(s):  
MOHAMMAD JALALI VARNAMKHASTI ◽  
LAI SOON LEE
Keyword(s):  
Genetic Algorithm ◽  
Sexual Selection ◽  
Hamming Distance ◽  
Knapsack Problems ◽  
Fitness Value ◽  
Tournament Selection ◽  
Males And Females ◽  
A New Technique ◽  
Selection Mechanisms ◽  
Active Genes

In this study, a new technique is presented for choosing mate chromosomes during sexual selection in a genetic algorithm. The population is divided into groups of males and females. During the sexual selection, the female chromosome is selected by the tournament selection while the male chromosome is selected based on the hamming distance from the selected female chromosome, fitness value or active genes. Computational experiments are conducted on the proposed technique and the results are compared with some selection mechanisms commonly used for solving multidimensional 0/1 knapsack problems published in the literature.

PALMPRINTS: A COOPERATIVE CO-EVOLUTIONARY ALGORITHM FOR CLUSTERING HAND IMAGES

2005 ◽  
Vol 05 (03) ◽  
pp. 595-616 ◽  
Author(s):  
NAWWAF KHARMA ◽  
CHING Y. SUEN ◽  
PEI F. GUO
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Practical Problem ◽  
Research Effort ◽  
Fitness Function ◽  
Image Clustering ◽  
Number Of Clusters ◽  
Fitness Value ◽  
Evolutionary Genetic ◽  
Hand Image

The main objective of Project PalmPrints is to develop and demonstrate a special co-evolutionary genetic algorithm (GA) that optimizes (a clustering fitness function) with respect to three quantities, (a) the dimensions of the clustering space; (b) the number of clusters; and (c) and the locations of the various clusters. This genetic algorithm is applied to the specific practical problem of hand image clustering, with success. In addition to the above, this research effort makes the following contributions: (i) a CD database of (raw and processed) right-hand images; (ii) a number of novel features designed specifically for hand image classification; (iii) an extended fitness function, which is particularly suited to a dynamic (i.e. dimensionality varying) clustering space. Despite the complexity of the multi-optimizational task, the results of this study are clear. The GA succeeded in achieving a maximum fitness value of 99.1%; while reducing the number of dimensions (features) of the space by more than half (from 84 to 41).

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