Saturday, November 15, 2008

Genetic Algorithm (GA) In Solving Multi Variants Problem Implemented In Ms. NET C# (Multivariate Genetic Algorithm Solution)

Year: 2008
Programming Language & Tools: Microsoft .NET C#
Source code / Request for customization:
Application For Test: Download

Optimization Using Genetic Algorithm In C#
This is a Multivariate Analysis and Solver using Genetic Algorithm methodology. Simulation application implemented in Ms. Net C#.

Assume we have certain number of farm lands (in area). Each land contain 2 types of insect. Assume we have certain number of fertilizer to be chosen to use where each of them having different cost (per area) and killing effectiveness (in %) for each type of insects.

By using Genetic Algorithm and Dynamic Programing technique, we want to find out, which fertilizer used for each land that satisfied minimum insects of number and also minimum cost of fertilizer.

Imagine if you have 50 farms and 30 types of fertilizer to choose. In order for you to obtain a promising solution by examining all possible combination, it will take years in the computational power we having now.

Using GA in solving multi-variant problem like this need a suitable data structure to represent the problem space. Representation of chromosome and DNA (element in each chromosome) need to be designed in a way to combine different farm lands with fertilizer. By randomly pair the farm land with the fertilizer, checking the fitness value in each chromosome, performing GA operation process to obtain a best fitness value chromosome. Then, the solution set can be obtain by retrieving the information in DNA from the chromosome.

You can go to: GA Project Coding Service for requesting the methodology document in details.

For understand another example of GA in solving shortest path problem, refer to:
Genetic Algorithm (GA) In Solving Vehicle Routing Problem

or access Genetic Algorithm (GA) In Solving Vehicle Routing Software Package

Genetic Algorithm process:
1. Perform a number of loops according to the generation.
2. For each loop:
a. Depend on the chance, perform Cross-Over operation.
b. Depend on the chance, Perform Overlapping operation.
c. Depend on the chance, perform Mutation operation.
d. Perform sort operation on the chromosome list.

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