Network design using Genetic Algorithm. Contents. Chapter 1. Chapter 2. Chapter 3. Chapter 4. Chapter 5. Chapter 6. Chapter 7. Chapter 8. JGAP: Java GA Package. Traveling salesman problem. [Documentation Index] The traveling salesman problem is the following: given a finite number of 'cities' along with the cost of travel between each pair of them, find the cheapest way of visiting all the cities and returning to your starting point.
This task has deserved a serious attention from mathematicians, and a lot of literature is available. The number of the possible routes increases rapidly when increasing the number of cities to visit, and the method of force become inappropriate. Genetic methods cannot find surely the best solution, but they can find the comparatively good solution in an acceptable time. JGAP implements the solution of this task using swap mutations and greedy crossover algorithm, as it is described by (Grefenstette et al, 1985). To solve this task with JGAP, you must define the specific of your task by deriving class from org.jgap.impl.Salesman. The most critical information you need to specify is how to measure the travel cost between the two cities. Introduction to GA. These pages introduce some fundamentals of genetic algorithms.
Pages are intended to be used for learning about genetic algorithms without any previous knowledge from this area. Only some knowledge of computer programming is assumed. You can find here several interactive Java applets demonstrating work of genetic algorithms. As the area of genetic algorithms is very wide, it is not possible to cover everything in these pages. But you should get some idea, what the genetic algorithms are and what they could be useful for.
Now please choose next to continue or you can choose any topic from the menu on the left side. JAGA: Java API for GA. Java GA Toolkit. A general GA toolkit implemented in Java, for experimenting with genetic algorithms and handling optimization problems Contents The GAA Applet/Application Examples and Test Problems Overview The GA Playground is a general purpose genetic algorithm toolkit where the user can define and run his own optimization problems.
The GA Playground is primarily designed to be used as an application and not as an applet, since it requires re-compiling of at least one class and use of local file I/O. Browser Requirements and Loading Times The applet is written in JDK 1.1.5 and uses the new event model. Updated info (2003): The program requires JDK between 1.1.5 and 1.4 (it will not run under JDK 1.4 or higher). The applet is large and takes a relatively long time to load. General Notes Alphabet The implementation of the genetic algorithm uses a high alphabet to encode the chromosome's genes. Problem Definition - Definition Files Problem Definition - Source Modifications Special GA Mechanisms Graphic Display. Example of a GA working in 2D search-space.
Genetic Algorithms - an Intuitive Introduction This Java-applet demonstrates the principle of a genetic algorithm (GA).
First, a fractal landscape is built. You may launch this with the button Landschaft (=landscape; in German the sch is spelled like the English sh). It's the GA's task to find the highest point of the landscape by "evaluating" the least possible number of points, that is, to measure the height of the landscape at as few points as possible. This is done with a population of let's say 100 bugs (individuals) randomly distributed over the landscape (press the button Population). Then the two steps of the GA, recombination and selection, are performed alternatingly: The recombination randomly selects two bugs as parents, preferring the ones with high fitness. You may run this evolution either in single-step-mode or run-mode: Pressing the button Rekombination/Selektion (it changes its label after every single step) triggers the next step, which is animated.
GA Demo. Genetic Algorithms and the Traveling Salesman Problem. Contents Genetic Algorithm Theory GA and TSP Base implementation, Template class GA<> and GA Selection classes Genome of Travel TSP Application GA thread UI interface Environment Reference Disclaimer.
