Compact Differential Evolution

De Montfort University Open Research Archive

Show simple item record Mininno, Ernesto en Neri, Ferrante en Cupertino, Francesco en Naso, David en 2012-04-03T11:10:59Z 2012-04-03T11:10:59Z 2011
dc.identifier.citation Mininno, E., Neri, F., Cupertino, F. and Naso, D. (2011), Compact Differential Evolution. IEEE Transactions on Evolutionary Computation, 15, (1), pp 32-54 en
dc.identifier.issn 1089-778X
dc.description.abstract This paper proposes the compact differential evolution (cDE) algorithm. cDE, like other compact evolutionary algorithms, does not process a population of solutions but its statistic description which evolves similarly to all the evolutionary algorithms. In addition, cDE employs the mutation and crossover typical of differential evolution (DE) thus reproducing its search logic. Unlike other compact evolutionary algorithms, in cDE, the survivor selection scheme of DE can be straightforwardly encoded. One important feature of the proposed cDE algorithm is the capability of efficiently performing an optimization process despite a limited memory requirement. This fact makes the cDE algorithm suitable for hardware contexts characterized by small computational power such as micro-controllers and commercial robots. In addition, due to its nature cDE uses an implicit randomization of the offspring generation which corrects and improves the DE search logic. An extensive numerical setup has been implemented in order to prove the viability of cDE and test its performance with respect to other modern compact evolutionary algorithms and state-of-the-art population-based DE algorithms. Test results show that cDE outperforms on a regular basis its corresponding population-based DE variant. Experiments have been repeated for four different mutation schemes. In addition cDE outperforms other modern compact algorithms and displays a competitive performance with respect to state-of-the-art population-based algorithms employing a DE logic. Finally, the cDE is applied to a challenging experimental case study regarding the on-line training of a nonlinear neuralnetwork-based controller for a precise positioning system subject to changes of payload. The main peculiarity of this control application is that the control software is not implemented into a computer connected to the control system but directly on the micro-controller. Both numerical results on the test functions and experimental results on the real-world problem are very promising and allow us to think that cDE and future developments can be an efficient option for optimization in hardware environments characterized by limited memory. en
dc.language.iso en en
dc.subject adaptive systems en
dc.subject compact genetic algorithms en
dc.subject differential evolution (DE) en
dc.subject estimation distribution algorithms en
dc.title Compact Differential Evolution en
dc.type Article en
dc.researchgroup Centre for Computational Intelligence en
dc.ref2014.selected 1367395509_1210680252775_11_2

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