Results And Conclusions:Future Scope

Initially we have tried with the number of population is twice of the number of the machines which result the earlier convergence of the problem. Due to this there may be possible of stuck at local optima. So we fixed up the number of population to 30. It gives better result than the former.
For the further improvement, we gave attention to the selection parameter.

How our team approached to Job Shop Scheduling using Genetic Algorithm

As said in previous post below is our approach for the Job Shop Scheduling using Genetic Algorithm

Project Team: Kumaravel.S, Mohammed Ishaq.I, Sankaralingam.B, Venkatesh.G

As first to generate the chromosomes, we generate the Random number from

Applications of Genetic Algorithms

• Automated design, including research on composite material design and multi-objective design of automotive components for crashworthiness, weight savings, and other characteristics.
  
• Automated design of mechatronic systems using bond graphs and genetic programming (NSF).
  
• Calculation of Bound States and Local Density Approximations.