Compact differential evolution light: high performance despite limited memory requirement and modest computational overhead
dc.cclicence | CC-BY-NC | en |
dc.contributor.author | Iacca, Giovanni | en |
dc.contributor.author | Caraffini, Fabio | en |
dc.contributor.author | Neri, Ferrante | en |
dc.date.accessioned | 2016-03-31T09:56:44Z | |
dc.date.available | 2016-03-31T09:56:44Z | |
dc.date.issued | 2012-09-01 | |
dc.description.abstract | Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of population-based algorithms by means of a probabilistic representation of the population of candidate solutions. These algorithms have a similar behaviour with respect to population-based algorithms but require a much smaller memory. This feature is crucially important in some engineering applications, especially in robotics. A high performance compact algorithm is the compact Differential Evolution (cDE) algorithm. This paper proposes a novel implementation of cDE, namely compact Differential Evolution light (cDElight), to address not only the memory saving necessities but also real-time requirements. cDElight employs two novel algorithmic modifications for employing a smaller computational overhead without a performance loss, with respect to cDE. Numerical results, carried out on a broad set of test problems, show that cDElight, despite its minimal hardware requirements, does not deteriorate the performance of cDE and thus is competitive with other memory saving and population-based algorithms. An application in the field of mobile robotics highlights the usability and advantages of the proposed approach. | en |
dc.funder | Academy of Finland | en |
dc.identifier.citation | Iacca, G., Caraffini, F. and Neri, F. (2012) Compact differential evolution light: high performance despite limited memory requirement and modest computational overhead. Journal of Computer Science and Technology, 27 (5), pp.1056-1076 | en |
dc.identifier.doi | https://doi.org/10.1007/s11390-012-1284-2 | |
dc.identifier.uri | http://hdl.handle.net/2086/11740 | |
dc.language.iso | en_US | en |
dc.peerreviewed | Yes | en |
dc.projectid | Algorithmic Issues in Memetic Computing | en |
dc.publisher | Springer US | en |
dc.researchgroup | Centre for Computational Intelligence | |
dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
dc.subject | Differential Evolution | en |
dc.subject | Optimisation | en |
dc.title | Compact differential evolution light: high performance despite limited memory requirement and modest computational overhead | en |
dc.type | Article | en |