Abstract
Evolutionary Computation (EC) approaches are known to empirically solve NP-hard optimisation problems. However, the genetic operators in these approaches have yet to be fully investigated and exploited for further improvements. Hence, we propose a novel genetic operator called Dynamic Programming Gene Transfer (DPGT) operator to improve the existing gene transfer operator in the Bacterial Memetic Algorithm (BMA). DPGT integrates dynamic programming based edit distance comparisons during gene transfer operator in BMA. DPGT operator enforces good gene transfers between individuals by conducting edit distance checks before transferring the genes. We tested the DPGT operator in an artificial learning agent ant’s perception-action problem. The experimental results revealed that DPGT gained overall improvements of training accuracy without any significant impact to the training processing time.
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Tang, T.Y., Egerton, S., Botzheim, J., Kubota, N. (2014). Dynamic Programming for Guided Gene Transfer in Bacterial Memetic Algorithm. 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_72
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DOI: https://doi.org/10.1007/978-3-319-12643-2_72
Publisher Name: Springer, Cham
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