An empirical assessment of local and population based search methods with different degrees of pseudorandomness
Markus Maucher, Uwe Schöning, Hans A. Kestler · OPen Access Repositorium der Universität Ulm (OPARU) (Ulm University) · 2008
When designing and analyzing randomized algorithms, one usually assumes that a sequence of uniformly distributed, independent random variables is available as a source of randomness. Implementing these algorithms, however, one has to use pseudorandom numbers. The quality of the used pseudorandom number generator may severely influence the quality of an algorithm’s output. We examined the effect of using low quality pseudorandom numbers on the performance of different search heuristics like Simulated Annealing and a basic evolutionary algorithm.