Abstract
Application of Quantum principles on evolutionary algorithms was started as early as late 1990s and has witnessed continued improvements since then. Following the same quantization principle introduced by the Quantum inspired evolutionary algorithm (QEA) in 2003, most of the existing quantum inspired algorithms focused mainly on evolving a single set of homogeneous solutions. In this paper, we present a new quantization process. In particular, aimed at solving numerical optimization problems, the evolutionary selection procedure is quantified through a set of subsolution points that jointly define candidate solutions. Implementing this new method on competitive co-evolution algorithm (CCEA), a new Quantum inspired competitive coevolution algorithm (QCCEA) is proposed in this paper. QCCEA is experimentally compared with CCEA through 9 benchmark numerical optimization functions published in CEC 2013. The results confirmed that QCCEA is more effective than CCEA over a majority of benchmark problems.
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Tirumala, S.S., Chen, G., Pang, S. (2014). Quantum Inspired Evolutionary Algorithm by Representing Candidate Solution as Normal Distribution. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_38
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DOI: https://doi.org/10.1007/978-3-319-12643-2_38
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12642-5
Online ISBN: 978-3-319-12643-2
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