Abstract
Multi expression programming is a linear genetic programming that dynamically determines its output from a series of genes of the chromosome. It works on a fixed-length individual, but gives rise to the complexity of the decoding process and fitness computations. To solve this problem, we proposed an improved algorithm that can speed up individual assessments through reuse analysis of evaluations. The experimental result shows that the present approach performs quite well on the considered problems.
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Deng, W., He, P. (2012). Improving Multi Expression Programming Using Reuse-Based Evaluation. In: Li, Z., Li, X., Liu, Y., Cai, Z. (eds) Computational Intelligence and Intelligent Systems. ISICA 2012. Communications in Computer and Information Science, vol 316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34289-9_33
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DOI: https://doi.org/10.1007/978-3-642-34289-9_33
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