Characterizing Fault Tolerance in Genetic Programming

摘要

Evolutionary Algorithms (Eas), and particularly Genetic Programming (GP), are techniques frequently employed to solve difficult real-life problems, which can require up to days or months of computation. One approach to reduce the time to solution is to use parallel computing on distributed platforms. Distributed platforms are prone to failures, and when these platforms are large and/or low-cost, failures are expected events rather than catastrophic exceptions. Therefore, fault tolerance and recovery techniques often become necessary. It turns out that Parallel GP (PGP) applications have an inherent ability to tolerate failures. This ability is quantified via simulation experiments performed using failure traces from real-world distributed platforms, namely, desktop grids (DGs), for two well-known GP problems. A simple technique is then proposed by which PGP applications can better tolerate the different, and often high, failures rates seen in different platforms.