Title: MFGA: a GA for complex real-world optimisation problems

Authors: Alessandro Turco, Carlos Kavka

Addresses: ESTECO Srl, AREA Science Park, Padriciano 99, 34149 Trieste, Italy. ' ESTECO Srl, AREA Science Park, Padriciano 99, 34149 Trieste, Italy

Abstract: We present a multi-objective genetic algorithm called magnifying front genetic algorithm (MFGA) designed in order to treat complex real-world optimisation problems. A first source of complexity is the presence of different input variables classes (real, discrete and categorical). MFGA is able to treat appropriately each of them as well as any combination. Moreover, real-world applications often require a long time to evaluate objective values from input variables. We deal with this issue working on elitism (in order to tune properly the balance between explorative and exploitative capabilities of the algorithm) and introducing a parallel steady-state evolution scheme, which is able to use the available computing resources as much intensively as possible. We test the algorithm on two different scenarios: mathematical benchmarks and real-world applications. For the latter one we chose a problem arising in multi-processor system-on-chip (MPSoC) design, a field which is characterised by discrete and more often categorical variables.

Keywords: elitism; genetic algorithms; multi-objective optimisation; SoC design; complexity; multi-processor system-on-chip.

DOI: 10.1504/IJICA.2011.037949

International Journal of Innovative Computing and Applications, 2011 Vol.3 No.1, pp.31 - 41

Published online: 21 Mar 2015 *

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