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