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EN
The aim of this paper is to present an implementation of Hierarchic Genetic Strategy (HGS) in solving the Permutation Flowshop Scheduling Problem (PFSP). We defined a hierarchic scheduler based on HGS structure for the exploration of the wide and complicated optimization landscape studied by Reeves. The objective of our work is to examine several variations of HGS operators in order to identify a configuration of operators and parameters that works best for the problem. From the experimental study we observed that HGS implementation outperforms existing schedulers in many of considered instances of a static benchmark for the problem.
EN
The scope of this paper is the construction of Hierarchical Genetic Strategy theoretical model based on the L-systems framework. The model was defined in the case of inactive prefix comparison procedure of HGS. We applied Vose's theory in a short formal analysis of basic search mechanisms implemented in the strategy.
EN
We presented the new hp-UGS (hp adaptive FEM, Hierarchical Genetic Strategy) multi-deme, genetic strategy which cau be used for solving parametric inverse problems formulated as the global optimization ones. Its efficiency follows from the coupled adaptation of accuracy derived from the proper balance between the accuracy of hp-FEM used for solving direct problem and the accuracy of solving optimization problem. It is shown, that hp-HGS can find at least the same set of local extremes as the Simple Genetic Algorithm (SGA). Moreover, the results of asymptotic analysis that verify much less computational cost of hp-HGS are recalled from the previous papers.
EN
We present a parallel hierarchical evolutionary strategy HGSNash as a new method of detecting the Nash equilibria in n-person non-cooperative games. The problem of finding the equilibrium points is formulated as a global optimization problem. A definition of the strategy and results of some simple numerical experiments are also included.
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