Prof Joanna Kolodziej, Department of Mathematics and Computer Science, University of Bielsko-Biala, Poland
Genetic search reinforced by the population hierarchy: Hierarchic Genetic Strategy (HGS)
As a result of their ability to deliver high quality solutions in reasonable time, Meta-heuristics are usually employed as effective methods to solve the complex multi-objective optimization problems. One class of such meta-heuristics is Hierarchic Genetic Strategy (HGS). A Genetic Algorithm variant, HGS differs from other genetic methods in its capability of searching concurrently the solution space. The HGS efficiency is therefore produced by the simultaneous execution of many dependent evolutionary processes. Every single process is then interpreted as the branch in a tree structure and can be defined as a sequence of evolving populations. The overall dependency relation among processes has a restricted number of levels. In this talk we present the theoretical and experimental evaluation of HGS in solving various complex multi-objective optimisation problems in discrete and continuous domains. In particular, the application of the strategy in scheduling the independent tasks in Computational Grids is highlighted.
School of Computing, Robert Gordon University, St Andrew Street, Aberdeen, Lecture Room A23, 14:00 – 15:00.