Abstract
It is easy to create parallel genetic algorithm software with master-slave type paralelization on a cluster of workstations. In a real situation the probability of errors in communication or in some of the slave processes during a long calculation is significant.In this paper we deal with different error handling strategies in master-slave type paralelization of standard GA algorithms and show results of test calculations. Our simulations are close to real applications in the sense that we examine the best achieved objective function value at a fixed wall clock time with different error handling strategies depending on the probability of errors and number of processors. Using these results we make suggestions on the selection of a good error handling method in different optimization problems.
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