scholarly journals Three-Dimensional Genetic Algorithm for the Kirkman's Schoolgirl Problem

MENDEL ◽  
2018 ◽  
Vol 24 (1) ◽  
pp. 25-30
Author(s):  
Jan Merta ◽  
Tomas Brandejsky
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Heuristic Method ◽  
Three Dimensional ◽  
Genetic Operators ◽  
Convergence Of An Algorithm ◽  
Literature Overview ◽  
Simple Heuristic ◽  
First Results ◽  
Incorrect Solution

This paper focuses on the possibilities of multidimensional genetic algorithms and relevant genetic operators. After the literature overview we used a three-dimensional genetic algorithm to solve a combinatorial task called Kirkman’s Schoolgirl Problem. The first results were not good, but we improved convergence of an algorithm by adding more genetic operators. We also used problem dependent mutation, where we tried to repair the incorrect solution by using the simple heuristic method. Finally, we got a solid number of correct solutions, but we know there is enough room for other improvements.

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 Scheduling Courses using Genetic Algorithms

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Schedule ◽  
Heuristic Method ◽  
Biological Evolution ◽  
Time Data ◽  
Fitness Evaluation ◽  
Class Schedule ◽  
Student College ◽  
Crossover And Mutation

Preparation of courses at every university is done by hand. This method has limitations that often cause collisions schedule. In lectures and lab scheduling frequent collision against the faculty member teaching schedule, collisions on the class schedule and student, college collision course with lab time, the allocation of the use of the rooms were not optimal. Heuristic method of genetic algorithm based on the mechanism of natural selection; it is a process of biological evolution. Genetic algorithms are used to obtain optimal schedule that consists of the initialization process of the population, fitness evaluation, selection, crossover, and mutation. Data used include the teaching of data, the data subjects, the room data and time data retrieved from the database of the Faculty of Computer Science, Universitas Pembangunan Panca Budi. The data in advance through the stages of the process of genetic algorithms to get optimal results The results of this study in the form of a schedule of courses has been optimized so that no error occurred and gaps.

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.

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.

scholarly journals Tuning Genetic Algorithm Parameters to Improve Convergence Time

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Maria Angelova ◽  
Tania Pencheva
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Method ◽  
Convergence Time ◽  
Generation Gap ◽  
Genetic Operators ◽  
Time Saving ◽  
Fermentation Processes ◽  
Highly Nonlinear ◽  
Crossover And Mutation

Fermentation processes by nature are complex, time-varying, and highly nonlinear. As dynamic systems their modeling and further high-quality control are a serious challenge. The conventional optimization methods cannot overcome the fermentation processes peculiarities and do not lead to a satisfying solution. As an alternative, genetic algorithms as a stochastic global optimization method can be applied. For the purpose of parameter identification of a fed-batch cultivation ofS. cerevisiaealtogether four kinds of simple and four kinds of multipopulation genetic algorithms have been considered. Each of them is characterized with a different sequence of implementation of main genetic operators, namely, selection, crossover, and mutation. The influence of the most important genetic algorithm parameters—generation gap, crossover, and mutation rates has—been investigated too. Among the considered genetic algorithm parameters, generation gap influences most significantly the algorithm convergence time, saving up to 40% of time without affecting the model accuracy.

The Simulation and Analyses of Timing-Optimal Trajectory Planning for 6DOF Robot

2014 ◽  
Vol 716-717 ◽  
pp. 1555-1558
Author(s):  
Zhi Jian Gou
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
Global Search ◽  
Search Performance ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Optimal Trajectory Planning

The algorithm has been improved to the adaptive genetic operators and flow based on the basic theory of simple genetic algorithm and adopted elitism strategy to select the best individual for iterative operation. The improved genetic algorithm not only ensured better global search performance, but also improved the convergent speed. The optimal solution was obtained and simulated by the improved genetic algorithms under the kinematical constraints.

New Hardware Engine for New Genetic Algorithms

2012 ◽  
Vol 490-495 ◽  
pp. 1831-1838
Author(s):  
Fariborz Ahmadi ◽  
Reza Tati
Keyword(s):  
Genetic Algorithm ◽  
Genetic Diversity ◽  
Genetic Algorithms ◽  
Hardware Implementation ◽  
Global Optimum ◽  
Local Optimum ◽  
Genetic Operators ◽  
Soft Computing Method ◽  
Software Implementations ◽  
Computing Method

Genetic algorithm is a soft computing method that works on set of solutions. These solutions are called chromosome and the best one is the absolute solution of the problem. The main problem of this algorithm is that after passing through some generations, it may be produced some chromosomes that had been produced in some generations ago that causes reducing the convergence speed. From another respective, most of the genetic algorithms are implemented in software and less works have been done on hardware implementation. Our work implements genetic algorithm in hardware that doesn’t produce chromosome that have been produced in previous generations. In this work, most of genetic operators are implemented without producing iterative chromosomes and genetic diversity is preserved. Genetic diversity causes that not only don’t this algorithm converge to local optimum but also reaching to global optimum. Without any doubts, proposed approach is so faster than software implementations. Evaluation results also show the proposed approach is faster than hardware ones.

Optimization of Genetic Operators for Scheduling Problems

2007 ◽  
Vol 11 (9) ◽  
pp. 1092-1098 ◽  
Author(s):  
António Ferrolho ◽  
◽  
Manuel Crisóstomo ◽  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Job Scheduling ◽  
Software Tool ◽  
Scheduling Problems ◽  
Genetic Operators ◽  
Mutation Operators ◽  
Crossover And Mutation

Genetic algorithms (GA) can provide good solutions for scheduling problems. But, when a GA is applied to scheduling problems various crossovers and mutations operators can be applicable. This paper presents and examines a new concept of genetic operators for scheduling problems. A software tool called hybrid and flexible genetic algorithm (HybFlexGA) was developed to examine the performance of various crossover and mutation operators by computing simulations of job scheduling problems.

The Research of Timing-Optimal Trajectory Planning Based on Improved Genetic Algorithms

2013 ◽  
Vol 433-435 ◽  
pp. 562-565 ◽  
Author(s):  
Zhi Jian Gou
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
Search Performance ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Operation Process ◽  
Optimal Trajectory Planning

The algorithm has been improved to the adaptive genetic operators and flow based on the basic theory of simple genetic algorithm and adopted elitism strategy to select the best individual for iterative operation. Program the operation process in the MATLAB software. The improved genetic algorithm not only ensured better global search performance, but also improved the convergent speed. The optimal solution was obtained by the improved genetic algorithms under the kinematical constraints.

Sign in / Sign up

Export Citation Format

Share Document