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DC Field | Value | Language |
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dc.contributor.author | Tinos, R | - |
dc.contributor.author | Yang, S | - |
dc.date.accessioned | 2011-09-26T10:58:47Z | - |
dc.date.available | 2011-09-26T10:58:47Z | - |
dc.date.issued | 2008 | - |
dc.identifier.citation | The 2008 IEEE Congress on Evolutionary Computation, Hong Kong: 1823 - 1830, 01 - 06 Jun 2008 | en_US |
dc.identifier.isbn | 978-1-4244-1822-0 | - |
dc.identifier.uri | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4631036 | en |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/5860 | - |
dc.description | This article is posted here with permission from IEEE - Copyright @ 2008 IEEE | en_US |
dc.description.abstract | The use of evolutionary programming algorithms with self-adaptation of the mutation distribution for dynamic optimization problems is investigated in this paper. In the proposed method, the q-Gaussian distribution is employed to generate new candidate solutions by mutation. A real parameter q, which defines the shape of the distribution, is encoded in the chromosome of individuals and is allowed to evolve. Algorithms with self-adapted mutation generated from isotropic and anisotropic distributions are presented. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutation on three dynamic optimization problems. | en_US |
dc.description.sponsorship | This work was supported by Brazil FAPESP under Grant 04/04289-6 and UK EPSRC under Grant No. EP/E060722/01. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Anisotropic magnetoresistance | en_US |
dc.subject | Biological cells | en_US |
dc.subject | Dynamic programming | en_US |
dc.subject | Entropy | en_US |
dc.subject | Gaussian distribution | en_US |
dc.subject | Genetic mutations | en_US |
dc.subject | Genetic programming | en_US |
dc.subject | Probability distribution | en_US |
dc.subject | Shape control | en_US |
dc.subject | Testing | en_US |
dc.title | Evolutionary programming with q-Gaussian mutation for dynamic optimization problems | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.doi | http://dx.doi.org/10.1109/CEC.2008.4631036 | - |
pubs.organisational-data | /Brunel | - |
pubs.organisational-data | /Brunel/Brunel (Active) | - |
pubs.organisational-data | /Brunel/Brunel (Active)/School of Info. Systems, Comp & Maths | - |
pubs.organisational-data | /Brunel/Research Centres (RG) | - |
pubs.organisational-data | /Brunel/Research Centres (RG)/CIKM | - |
pubs.organisational-data | /Brunel/School of Information Systems, Computing and Mathematics (RG) | - |
pubs.organisational-data | /Brunel/School of Information Systems, Computing and Mathematics (RG)/CIKM | - |
Appears in Collections: | Publications Computer Science Dept of Computer Science Research Papers |
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