Authors:
Ivan Sekaj
;
Marek Linder
and
Daniel Pernecký
Affiliation:
Institute of Control and Industrial Informatics, Faculty of Electrical Engineering and Information Technology and Slovak University of Technology, Slovak Republic
Keyword(s):
Evolutionary Algorithm, Genetic Algorithm, Parallelisation, Architecture, Migration, Overlapping, Experimental Comparison.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
Parallel evolutionary algorithms are able to improve the performance of simple evolutionary algorithms which use a single population. Their characteristics and performance depend on their architectures and other factors and parameters. In our contribution we present some viewpoints of classification and we demonstrate experimentally the influence of selected factors such as architecture type, migration topology, migration period, number of migrants, numbers of subpopulations, subpopulation size and others on the performance of these algorithms. This experimental study should help to generalise the properties and behaviour of various types of parallel evolutionary algorithms and help to design algorithms for solving hard search/optimisation problems like modelling of bio-medicine processes, optimisation of pharmaceutical dosing, optimisation of large technological and construction tasks etc.