Objective
The increasing complexity of design in high technology industry requires more and more robust optimisation and control tools which should accept simultaneously discrete, continuous and combinatorial situations. Like living organisms subject to Darwin's rules of evolution a new computer technology called Genetic Algorithrns and Evolution Strategies consists of simple lines of code representing a population of candidate individuals which through chance matings, crossing over of digital DNA and mutation evolve sharing information and producing novel combinations for finding global solutions in a complex industrial environment where traditional methods may fail The main goal of the Network is to bring together academic and industrial node partners in order to evaluate compare in terms of accuracy and efficiency performances of recent evolution based methodologies and (parallel) softwares on selected test problems of industrial interest. The kernel of the Network will consist of three major components: (1) the academic component, CEANI/University of Las Palmas coordinating the kernel of the network whose tasks will be the general management of the project, the organisational secretariat and electronic and informatic coordination of the research activities, (2) the technological component, rNRIA Sophia Antipolis, providing node-partners with database analysis tools for receiving continuously genetic data contributions from partners and (3) the industrial component, Dassault Aviation, for gathering and coordinating industrial node partners and bridging methodologies with selected industrial test problems fitting complex modern challenges in optimisation and control .
Funding Scheme
EAT - Exploratory awards (thematic networks)Coordinator
35017 Las Palmas De Gran Canaria - Telde
Spain
