Abstract
We investigate a modified Quantum Evolutionary method for solving real value problems. The Quantum Inspired Evolutionary Algorithms (QIEA) are binary encoded evolutionary techniques used for solving binary encoded problems and their signature feature follows superposition of multiple states on a quantum bit. This is usually implemented by sampling a binary chromosome string, according to probabilities stored in an underlying probability string. In order to apply this paradigm to real value problems, real QIEAs (rQIEA) were developed using real encoding while trying to follow the original quantum computing metaphor. In this paper we report the issues we encounter while implementing some of the published techniques. Firstly, we found that the investigated rQIEAs tend to stray from the original quantum computing interpretation, and secondly, their performance on a number of test problems was not as good as claimed in the original publications. Subsequently, we investigated further and developed binary QIEA for use with real value problems. In general, the investigated and designed quantum method for real-value problems, produced better convergence on most of the examined problems and showed very few inferior results.
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Wright, J., Jordanov, I. (2015). Quantum Evolutionary Methods for Real Value Problems. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_24
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DOI: https://doi.org/10.1007/978-3-319-19644-2_24
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