Abstract
The focus of this paper is variable length optimisation, which is a type of optimisation where the number of variables in the optimal solution is not known a priori. Due to the difference in solution space, traditional algorithms for fixed length problems either require significant adjustment, or cannot be applied at all. Furthermore, there is evidence that variable length algorithms - algorithms that consider solutions with different lengths throughout the optimisation process - may outperform fixed length algorithms on these problems. To investigate this, we have designed an abstract variable length problem that allows for straightforward and clear analysis. The performance of a number of evolutionary algorithms on this problem are analysed, including a fixed length algorithm and a state-of-the-art variable length algorithm. We propose a new mutation operator for variable length algorithms, and suggest potential directions for further research. Overall, the variable length algorithm with our mutation operator outperformed the state-of-the-art variable length algorithm, and the fixed length algorithm.
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Van Ryt, S., Gallagher, M., Wood, I. (2020). A Novel Mutation Operator for Variable Length Algorithms. In: Gallagher, M., Moustafa, N., Lakshika, E. (eds) AI 2020: Advances in Artificial Intelligence. AI 2020. Lecture Notes in Computer Science(), vol 12576. Springer, Cham. https://doi.org/10.1007/978-3-030-64984-5_14
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DOI: https://doi.org/10.1007/978-3-030-64984-5_14
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