Dynamic parameter control of interactive local search in UML software design

User-centered Interactive Evolutionary Computation (IEC) has been applied to a wide variety of areas, including UML software design. The performance of evolutionary search is important as user interaction fatigue remains an on-going challenge in IEC. However, to obtain optimal search performance, it is usually necessary to “tune” evolutionary control parameters manually, although tuning control parameters can be time-consuming and error-prone. To address this issue in other fields of evolutionary computation, dynamic parameter control including deterministic, adaptive and self-adaptive mechanisms have been applied extensively to real-valued representations. This paper postulates that dynamic parameter control may be highly beneficial to IEC in general, and UML software design in particular, wherein a novel object-based solution representation is used. Three software design problems from differing design domains and of differing scale have been investigated with mutation probabilities modified by simulated annealing, the Rechenberg “1/5 success rule” and self-adaptation within local search. Results indicate that self-adaptation appears to be the most robust and scalable mutation probability modification mechanism. The use of self-adaption with an object-based representation is novel, and results indicate that dynamic parameter control offers great potential within IEC.