LSSC2011 Optimization of intermolecular interaction potential energy parameters for Monte-Carlo and Molecular dynamics simulations using Genetic Algorithms (GA)
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Optimization of intermolecular interaction potential energy parameters for Monte-Carlo and Molecular dynamics simulations using Genetic Algorithms (GA)
![Optimization of intermolecular interaction potential energy parameters for Monte-Carlo and Molecular dynamics simulations using Genetic Algorithms (GA) Dragan Sahpaski [email_address] Institute of Informatics, Faculty of Natural Sciences University “Ss. Cyril and Methodius” Skopje, Macedonia Ljupco Pejov [email_address] Institute of Chemistry, Faculty of Natural Sciences University “Ss. Cyril and Methodius” Skopje, Macedonia Anasas Misev [email_address] Institute of Informatics, Faculty of Natural Sciences University “Ss. Cyril and Methodius” Skopje, Macedonia *This work is supported by the FP7 project HP-SEE](https://image.slidesharecdn.com/lssc2011-110607095447-phpapp01/85/LSSC2011-Optimization-of-intermolecular-interaction-potential-energy-parameters-for-Monte-Carlo-and-Molecular-dynamics-simulations-using-Genetic-Algorithms-GA-1-320.jpg)
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LSSC2011 Optimization of intermolecular interaction potential energy parameters for Monte-Carlo and Molecular dynamics simulations using Genetic Algorithms (GA)
- 1. Optimization of intermolecular interaction potential energy parameters for Monte-Carlo and Molecular dynamics simulations using Genetic Algorithms (GA) Dragan Sahpaski [email_address] Institute of Informatics, Faculty of Natural Sciences University “Ss. Cyril and Methodius” Skopje, Macedonia Ljupco Pejov [email_address] Institute of Chemistry, Faculty of Natural Sciences University “Ss. Cyril and Methodius” Skopje, Macedonia Anasas Misev [email_address] Institute of Informatics, Faculty of Natural Sciences University “Ss. Cyril and Methodius” Skopje, Macedonia *This work is supported by the FP7 project HP-SEE
- 13. Results with the standard LJ parameters
- 15. The Optimization Problem C Cl C Cl q ε σ
- 16. Outline of a Genetic Algorithm (GA) Create a random starting population of chromosomes Calculate the fitness of each chromosome Select the next generation Crossover Mutation ? >= N generations? YES END NO
- 18. Mutation 1 0 . . 0 1 0 . . GENE 1 1 1 0 . . 0 0 0 . . 1 1 MUTATED GENE FLIP a BIT in a Random position with a certain probability
- 19. Crossover Pick a random position and swap all subsequent genes in the two parents q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C
- 20. Selection Select the best individuals with higher probability: *Always select the fittest q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C q Cl ε Cl σ Cl ε C σ C
- 22. The Optimization Procedure on GRID