|Seminar video on Youtube|
Wednesday, December 5th, 2018. Time: 12 noon
Place: O.C. Zienkiewicz Conference Room, C1 Building, UPC Campus Nord, Barcelona
Genetic algorithms are optimization methods based on the Darwin's theory of the evolution of the species. This evolution is managed by genetic information compound only by the information of individual evaluations of the function of interest. This is one of the main drawbacks when comparing with Gradient-based methods. In order to overcome this issue, hybrid methods increase the genetic information defining several populations in parallel. These populations can be defined using the same parameters, or can be different. The present presentation explore the benefits of defining population from pretty similar, just slightly modifying few parameters, to completely different, using a completely new set-up. Applications to transport simulation, or CFD optimization will be presented to illustrate the results.
Mechanical engineer in aeronautical field; developing and applying new optimization techniques, robust design optimization, hierarchical methods, game strategies, uncertainty quantification, random number generation. But also aerostructural and aeroelasticity research as main focuses of my research career.
Industrial experience in technical project management, and project development, knowledge transfer, research and development; as well as experience in the creation of company network to upgrade the R+D in the industrial sector.
Specialties: Research in aeronautics, optimization, and stocasthics/uncertainty management.
Project management; team leadership, outsourcing management, customer care, bid preparation, consortium management and establisment.