On the Intrinsic Fault-Tolerance Nature of Parallel Genetic Programming

In this paper we show how parallel genetic programming can run on a distributed system with volatile resources without any lack of efficiency. By means of a series of experiments, we test whether parallel GP - and consistently evolutionary algorithms - are intrinsically fault-tolerant. The interest of this result is crucial for researchers dealing with real-life problems in which parallel and distributed systems are required for obtaining results on a reasonable time. In that case, parallel GP tools will not require the inclusion of fault-tolerant computing techniques or libraries when running on meta-systems undergoing volatility, such us desktop grids offering public resource computing. We test the performance of the algorithm by studying the quality of solutions when running over distributed resources undergoing processors failures, when compared with a fault-free environment. This new feature, which shows its advantages, improves the dependability of the parallel genetic programming algorithm