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A Performance Assessment of Evolutionary Algorithms in Volunteer Computing Environments: The Importance of Entropy

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10199)


In a volunteer distributed computing system, users run a program on their own machine to contribute to a common effort. If the program is embedded in a web page, collaboration is straightforward, but also ephemeral. In this paper, we analyze a volunteer evolutionary computing system called NodIO, by running several experiments, some of them massive. Our objective is to discover rules that encourage volunteer participation and also the interplay of these contributions with the dynamics of the algorithm itself, making it more or less efficient. We will show different measures of participation and contribution to the algorithm, as well as how different volunteer usage patterns and tweaks in the algorithm, such as restarting clients when a solution has been found, contribute to improvements and leveraging of these contributions. We will also try to find out what is the key factor in the early termination of the experiments, measuring entropy in the contributions and other large scale indicators.

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This work has been supported in part by TIN2014-56494-C4-3-P (Spanish Ministry of Economy and Competitivity). We are also grateful to @otisdriftwood for his help gathering users for the new experiments.

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Correspondence to Juan J. Merelo .

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Merelo, J.J., de las Cuevas, P., García-Sánchez, P., García-Valdez, M. (2017). A Performance Assessment of Evolutionary Algorithms in Volunteer Computing Environments: The Importance of Entropy. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham.

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