New parallel hybrid genetic algorithm based on molecular dynamics approach for energy minimization of atomistic systems

M. Celino, P. Palazzari, N. Pucello, M. Rosati, V. Rosato

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

A hybrid genetic algorithm (HGA) for the optimization of the ground-state structure of a metallic atomic cluster has been implemented on a MIMD-SIMD parallel platform. The concept of building block (BB) is generalized to cover this real-coded optimization problem. On the basis of some reasonings on the dependence of the convergence of Genetic Algorithms (GAs) from BBs, an hybrid genetic algorithm (HGA) is proposed to solve the minimization problem. All the elements of each new population are optimized through a Molecular Dynamics algorithm: the aim of MD is to create ever better BBs and, consequently, to improve the convergence of GAs. HGA has been implemented on a MIMD-SIMD platform based on the massively parallel processing supercomputer Quadrics/APE100 which offers a peak performance of 25.6 Gflops; we obtained a sustained computational power greater than 10 Gflops.
Original languageEnglish
Publication statusPublished - 1997
EventProceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC'97 - , Unknown
Duration: 1 Jan 1997 → …

Conference

ConferenceProceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC'97
CountryUnknown
Period1/1/97 → …

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

Cite this

Celino, M., Palazzari, P., Pucello, N., Rosati, M., & Rosato, V. (1997). New parallel hybrid genetic algorithm based on molecular dynamics approach for energy minimization of atomistic systems. Paper presented at Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, ICEC'97, Unknown.