Searching for Pareto-optimal Randomised Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Millard:2012:SSBSE,

• author = "Alan G. Millard and David R. White and John A. Clark",
• title = "Searching for Pareto-optimal Randomised Algorithms",
• booktitle = "4th Symposium on Search Based Software Engineering",
• year = "2012",
• editor = "Gordon Fraser and Jerffeson {Teixeira de Souza} and Angelo Susi",
• volume = "7515",
• series = "Lecture Notes in Computer Science",
• pages = "183--197",
• address = "Riva del Garda, Italy",
• month = sep # " 28-30",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, SBSE, MOGA",
• isbn13 = "978-3-642-33118-3",
• DOI = "doi:10.1007/978-3-642-33119-0_14",
• size = "15 pages",
• 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.",

}

Genetic Programming entries for Alan G Millard David Robert White John A Clark

Citations