Abstract
There are many optimization algorithms which can be used for solving different tasks. One of those is the genetic programming method, which can build an analytical function which can describe data. The function is coded in a tree structure. The problem is that when we decide to use lower maximal depth of the tree, the genetic programming is not able to compose a function which is good enough. This paper describes the way how to solve this problem. The approach is based on creating partial solutions represented by subtrees and composing them together to create the last tree. This approach was tested for finding a function which can correctly calculate the output according to the given inputs. The experiments showed that even when using a small maximal depth, the genetic programming using our approach can create functions with good results.
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Research supported by the National Research and Development Project Grant 1/0773/16 2016 – 2019 “Cloud Based Artificial Intelligence for Intelligent Robotics”.
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Cádrik, T., Mach, M. (2017). Genetic Programming Algorithm Creating and Assembling Subtrees for Making Analytical Functions. In: Matoušek, R. (eds) Recent Advances in Soft Computing. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 576. Springer, Cham. https://doi.org/10.1007/978-3-319-58088-3_6
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DOI: https://doi.org/10.1007/978-3-319-58088-3_6
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