Preferences help
enabled [disable] Abstract
Number of results
2015 | 13 | 66-87
Article title

Using Evolutionary Algorithm to Solve Amazing Problem by Two Ways: Coordinates and Locations

Title variants
Languages of publication
In the computer science field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a meta heuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. This paper studies the following problem: A square construction site is divided into 9 grid units. We need to use GAs to determine the best location of two temporary facilities A and B, so that: 1. Facility A is as close as possible to facility B; 2. Facility A is as close as possible to the fixed facility F.; 3. Facility B is as far as possible to the fixed facility F. We use two different ways to solve the problem, first by coordinates and second by grid locations. Experimentally the two way results shows that the genetic algorithm have the ability to find optimal solution or find solutions nearby optimal solutions.
Physical description
  • [1] R. C. Chakraborty. “Fandemantaels of genetic algorithms”: AI course lecture 39-40, notes, slides, 2010.
  • [2] Andrey Popov,” Genetic Algorithms For Optimization” Hamburg, 2 005.
  • [3] Lubna Zaghlul Bashir, Nada Mahdi, World Scientific News 5 (2015) 138-150.
  • [4] Goldberg, David E. “Genetic Algorithm in Search, Optimization, and Machine Learning”, Addison Wesley Longmont, International Student Edition 1989.
  • [5] Ms. Dharmistha D. Vishwakarma” Genetic Algorithm based Weights Optimization of Artificial Neural Network”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 1, Issue 3, August 2012.
  • [6] Olesya Peshko,”Global OptimizationGenetic Algorithms”, 2007.
  • [7] Larry Yaeger,” Intro to Genetic Algorithms”Artificial Life as an approach to Artificial Intelligence”, Professor of Informatics, Indiana University, 2008.
  • [8] Colin Reeves, “Genetic algorithms”, School of Mathematical and Information Sciences Coventry University Priory St Coventry CV1 5FBE-mail: C. Reeves@conventry., 2005.
  • [9] Mitchell Melanie,” An Introduction to Genetic Algorithms”, A Bradford Book The MIT Press Cambridge, Massachusetts London, England, Fifth printing, 1999.
  • [10] Saif Hasan, Sagar Chordia, Rahul Varshneya, ”Genetic algorithm”, February 6, 2012.
  • [11] Ashish Ghosh, Satchidananda Dehuri, ”Evolutionary algorithms for multi criterian optimization: a servay”, International journal of computing and information system Vol. 2, No 1, April 2004.
  • [12] From Wikipedia, the free encyclopedia.
  • [13] Lubna Zaghlul Bashir, Rajaa Salih Mohammed Hasan, World Scientific News 6 (2015) 41-56.
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.