SIMULATION OF GENETIC ALGORITHMS ON MIMD MULTICOMPUTERS

1992 ◽  
Vol 02 (04) ◽  
pp. 381-389 ◽  
Author(s):  
I. DE FALCO ◽  
R. DEL BALIO ◽  
E. TARANTINO ◽  
R. VACCARO
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Diffusion Processes ◽  
Travelling Salesman Problem ◽  
Test Results ◽  
Parallel Genetic Algorithm ◽  
Genetic Operators ◽  
Parallel Diffusion ◽  
Fully Connected ◽  
Chordal Ring

In this paper, a Parallel Genetic Algorithm has been developed and mapped onto a coarse grain MIMD multicomputer whose processors have been configured in a fully connected chordal ring topology. In this way, parallel diffusion processes of good local information among processors have been carried out. The Parallel Genetic Algorithm has been applied, specifically, to the Travelling Salesman Problem. Many experiments have been performed with different combinations of genetic operators; the test results suggest that PMX crossover can be avoided by using only the inversion genetic operator and that a diffusion process leads to improved search in Parallel Genetic Algorithms.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

scholarly journals Lecture Scheduling Automation Using Genetic Algorithm

2019 ◽  
Vol 8 (5C) ◽  
pp. 1444-1447
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Test Results ◽  
Scheduling Problems ◽  
Scheduling System ◽  
Parameter Values

This paper aims produce an academic scheduling system using Genetic Algorithm (GA) to solve the academic schedule. Factors to consider in academic scheduling are the lecture to be held, the available room, the lecturers and the time of the lecturer, the suitability of the credits with the time of the lecture, and perhaps also the time of Friday prayers, and so forth. Genetic Algorithms can provide the best solution for some solutions in dealing with scheduling problems. Based on the test results, the resulting system can automate the scheduling of lectures properly. Determination of parameter values in Genetic Algorithm also gives effect in producing the solution of lecture schedule

Using Genetic Algorithms to Set Target Values for Engineering Characteristics in the House of Quality

1999 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan Colton
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Penalty Functions ◽  
Preliminary Design ◽  
Test Results ◽  
House Of Quality ◽  
Design Specifications ◽  
Engineering Characteristics ◽  
Target Values

Abstract As part of a strategy for obtaining preliminary design specifications from the House of Quality, genetic algorithms were used to generate and optimize preliminary design specifications for an automotive case study. This paper describes the House of Quality for the automotive case study. In addition, the genetic algorithm chosen, the genetic coding, the methods used for mutation and reproduction, and the fitness and penalty functions are descrobed. Methods for determining convergence are examined. Finally, test results show that the genetic algorithm produces reasonable preliminary design specifications.

The Algorithm Animation of Genetic Algorithm of Travelling Salesman Problem

2013 ◽  
Vol 411-414 ◽  
pp. 2013-2016 ◽  
Author(s):  
Guo Zhi Wen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traveling Salesman Problem ◽  
Travelling Salesman Problem ◽  
Traveling Salesman ◽  
Travelling Salesman ◽  
Algorithm Animation ◽  
The Traveling Salesman Problem

The traveling salesman problem is analyzed with genetic algorithms. The best route map and tendency of optimal grade of 500 cities before the first mutation, best route map after 15 times of mutation and tendency of optimal grade of the final mutation are displayed with algorithm animation. The optimal grade is about 0.0455266 for the best route map before the first mutation, but is raised to about 0.058241 for the 15 times of mutation. It shows that through the improvements of algorithms and coding methods, the efficiency to solve the traveling problem can be raised with genetic algorithms.

scholarly journals Construct Linear Polynomial Complementary Transformation for NP-Completeness Using Parallel Genetic Algorithm

2016 ◽  
Author(s):  
Tarik Eltaeib ◽  
Julius Dichter
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Parallel Genetic Algorithm ◽  
Algorithm Optimization ◽  
Parallel Genetic Algorithms ◽  
Np Completeness ◽  
Linear Polynomial ◽  
Optimization Function ◽  
Speed Up ◽  
Complementary Equation

This paper examines the correlation between numbers of computer cores in parallel genetic algorithms. The objective to determine the linear polynomial complementary equation in order represent the relation between number of parallel processing and optimum solutions. Model this relation as optimization function (f(x)) which able to produce many simulation results. F(x) performance is outperform genetic algorithms. Compression results between genetic algorithm and optimization function is done. Also the optimization function give model to speed up genetic algorithm. Optimization function is a complementary transformation which maps a TSP given to linear without changing the roots of the polynomials.

scholarly journals A Master Slave Parallel Genetic Algorithm for Feature Selection in High Dimensional Datasets

2019 ◽  
Vol 8 (3) ◽  
pp. 379-384
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Feature Selection ◽  
Information Gain ◽  
Optimal Number ◽  
Good Choice ◽  
High Dimensional ◽  
Parallel Genetic Algorithm ◽  
Efficient Manner ◽  
High Dimensional Datasets

Feature Selection in High Dimensional Datasets is a combinatorial problem as it selects the optimal subsets from N dimensional data having 2N possible subsets. Genetic Algorithms are generally a good choice for feature selection in large datasets, though for some high dimensional problems it may take varied amount of time - few seconds, few hours or even few days. Therefore, it is important to use Genetic Algorithms that can give quality results in reasonably acceptable time limit. For this purpose, it is becoming necessary to implement Genetic Algorithms in an efficient manner. In this paper, a Master Slave Parallel Genetic Algorithm is implemented as a Feature Selection procedure to diminish the time intricacies of sequential genetic algorithm. This paper describes the speed gains in parallel Master-Slave Genetic Algorithm and also discusses the theoretical analysis of optimal number of slaves required for an efficient master slave implementation. The experiments are performed on three high-dimensional gene expression data. As Genetic Algorithm is a wrapper technique and takes more time to find the importance of any feature, Information Gain technique is used first as pre-processing task to remove the irrelevant features.

scholarly journals Computing strong metric dimension of some special classes of graphs by genetic algorithms

2008 ◽  
Vol 18 (2) ◽  
pp. 143-151 ◽  
Author(s):  
Jozef Kratica ◽  
Vera Kovacevic-Vujcic ◽  
Mirjana Cangalovic
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Graph Coloring ◽  
Crew Scheduling ◽  
Metric Dimension ◽  
Hard Problem ◽  
Genetic Operators ◽  
Strong Metric Dimension ◽  
Np Hard Problem ◽  
Special Classes

In this paper we consider the NP-hard problem of determining the strong metric dimension of graphs. The problem is solved by a genetic algorithm that uses binary encoding and standard genetic operators adapted to the problem. This represents the first attempt to solve this problem heuristically. We report experimental results for the two special classes of ORLIB test instances: crew scheduling and graph coloring.

Image Dimensionality Reduction Based on the Intrinsic Dimension and Parallel Genetic Algorithm

2011 ◽  
Vol 5 (2) ◽  
pp. 97-112 ◽  
Author(s):  
Liang Lei ◽  
TongQing Wang ◽  
Jun Peng ◽  
Bo Yang
Keyword(s):  
Genetic Algorithm ◽  
Dimensionality Reduction ◽  
Image Features ◽  
Similarity Function ◽  
Nonlinear Dimensionality Reduction ◽  
General Covariance ◽  
Parallel Genetic Algorithm ◽  
Web Content ◽  
Genetic Operators ◽  
Intrinsic Dimension

In the research of Web content-based image retrieval, how to reduce more of the image dimensions without losing the main features of the image is highlighted. Many features of dimensional reduction schemes are determined by the breaking of higher dimensional general covariance associated with the selection of a particular subset of coordinates. This paper starts with analysis of commonly used methods for the dimension reduction of Web images, followed by a new algorithm for nonlinear dimensionality reduction based on the HSV image features. The approach obtains intrinsic dimension estimation by similarity calculation of two images. Finally, some improvements were made on the Parallel Genetic Algorithm (APGA) by use of the image similarity function as the self-adaptive judgment function to improve the genetic operators, thus achieving a Web image dimensionality reduction and similarity retrieval. Experimental results illustrate the validity of the algorithm.

Using Genetic Algorithms to Set Target Values for Engineering Characteristics in the House of Quality

2002 ◽  
Vol 2 (2) ◽  
pp. 106-114 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan S. Colton
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Penalty Functions ◽  
Preliminary Design ◽  
Test Results ◽  
House Of Quality ◽  
Design Specifications ◽  
Engineering Characteristics ◽  
Target Values

As part of a strategy for obtaining preliminary design specifications from the House of Quality, genetic algorithms are used to generate and optimize preliminary design specifications for an automotive case study. This paper describes the House of Quality for an automotive case study. In addition, the genetic algorithm chosen, the genetic coding, the methods used for mutation and reproduction, and the fitness and penalty functions are described. Methods for determining convergence are examined. Finally, test results show that the genetic algorithm produces reasonable preliminary design specifications.

Sign in / Sign up

Export Citation Format

Share Document