An approach for compliance-aware service selection with genetic algorithms

Abstract

Genetic algorithms are popular for service selection as they deliver good results in short time. However, current approaches do not consider compliance rules for single tasks in a process model. To address this issue, we present an approach for compliance-aware service selection with genetic algorithms. Our approach employs the notion of compliance distance to detect and recover violations and can be integrated into existing genetic algorithms by means of a repair operation. As a proof-of-concept, we present a genetic algorithm incorporating our approach and compare it with related state-of-the-art genetic algorithms lacking this kind of check and recovery mechanism for compliance. © 2013 Springer-Verlag.