Sequential Optimization: Genetic Algorithm

2014 ◽  
pp. 63-86
Author(s):  
Sima Noghanian ◽  
Abas Sabouni ◽  
Travis Desell ◽  
Ali Ashtari
Keyword(s):  
Genetic Algorithm ◽  
Sequential Optimization

Meta-Heuristic Algorithms to Solve Bi-Criteria Parallel Machines Scheduling Problem

2016 ◽  
Vol 7 (2) ◽  
pp. 76-96 ◽  
Author(s):  
Kawal Jeet ◽  
Renu Dhir ◽  
Sameer Sharma
Keyword(s):  
Genetic Algorithm ◽  
Parallel Machines ◽  
Heuristic Algorithms ◽  
Statistical Tests ◽  
Parallel Machine Scheduling ◽  
Sequential Optimization ◽  
Scheduling Problems ◽  
Multi Objective ◽  
Bee Colony ◽  
Parallel Machines Scheduling

Parallel machine scheduling problems are classified as NP-hard problems. The direct solutions for these problems are not available and meta-heuristic algorithms are required to be used to find near-optimal solutions. In this paper, the formulations of multi-objective Artificial Bee Colony algorithm by using combination of weighted objectives, secondary storage for managing possible solutions and Genetic algorithm have been developed and applied to schedule jobs on parallel machines optimizing bi-criteria namely maximum tardiness and weighted flow time. The results obtained indicate that proposed algorithm outperforms other multi-objective algorithms in optimizing bi-criteria scheduling problems on parallel machines. Further, the sequential optimization of bi-criteria using Early Due Date (EDD) followed by Genetic Algorithm (GA) has also been investigated. The efficiencies of the proposed algorithms have been verified by numerical illustrations and statistical tests.

The ground state energy of the ±J spin glass from the genetic algorithm

1994 ◽  
Vol 4 (9) ◽  
pp. 1281-1285 ◽  
Author(s):  
P. Sutton ◽  
D. L. Hunter ◽  
N. Jan
Keyword(s):  
Genetic Algorithm ◽  
Spin Glass ◽  
Ground State Energy ◽  
Ground State ◽  
State Energy

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

Recycling Lethal Chromosomes Based on Immune Operation in Genetic Algorithm for Multi-Knapsack Problem

2014 ◽  
Vol 1 ◽  
pp. 219-222
Author(s):  
Jing Guo ◽  
Jousuke Kuroiwa ◽  
Hisakazu Ogura ◽  
Izumi Suwa ◽  
Haruhiko Shirai ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem
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