PROBABILISTIC PARALLEL PROGRAMMING BASED ON MULTISET TRANSFORMATION

The GAMMA parallel programming model is based on the multiset datastructure. Here, a succession of chemical reactions consume the elements of the multiset and produce new elements according to specific rules, This paper extends GAMMA model to its probabilistic version - called P-GAMMA model to realise evolutionary computations - namely probabilistic, classifier, bucket-brigade learning and genetic algorithms. We also explain how to support evolutionary computations through randomized choices and concurrent transformations on persistent globally accessible tuplespaces using query processing and transaction mechanisms.