Abstract
Randomised algorithms traditionally make stochastic decisions based on the result of sampling from a uniform probability distribution, such as the toss of a fair coin. In this paper, we relax this constraint, and investigate the potential benefits of allowing randomised algorithms to use non-uniform probability distributions. We show that the choice of probability distribution influences the non-functional properties of such algorithms, providing an avenue of optimisation to satisfy non-functional requirements. We use Multi-Objective Optimisation techniques in conjunction with Genetic Algorithms to investigate the possibility of trading-off non-functional properties, by searching the space of probability distributions. Using a randomised self-stabilising token circulation algorithm as a case study, we show that it is possible to find solutions that result in Pareto-optimal trade-offs between non-functional properties, such as self-stabilisation time, service time, and fairness.
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Millard, A.G., White, D.R., Clark, J.A. (2012). Searching for Pareto-optimal Randomised Algorithms. In: Fraser, G., Teixeira de Souza, J. (eds) Search Based Software Engineering. SSBSE 2012. Lecture Notes in Computer Science, vol 7515. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33119-0_14
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DOI: https://doi.org/10.1007/978-3-642-33119-0_14
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