Adaptive parameter control of evolutionary algorithms to improve quality-time trade-off

Parameter control of evolutionary algorithms (EAs) poses special challenges as EA uses a population and requires many parameters to be controlled for an effective search. Quality improvement is dependent on several factors, such as, fitness estimation, population diversity and convergence rate. A widely practiced approach to identify a good set of parameters for a particular class of problem is through experimentation. Ideally, the parameter selection should depend on the resource availability, and thus, a rigid choice may not be suitable. In this work, we propose an automated framework for parameter selection, which can adapt according to the constraints specified. To condition the parameter choice through resource constraint/utilization, we consider two typical scenarios, one where maximum available run-time is pre-specified and the other in which a utility function modeling the quality-time trade-off is used instead of a rigid deadline. We present static and dynamic parameter selection strategies based on a probabilistic profiling method. Experiments performed with traveling salesman problem (TSP) and standard cell placement problem show that an informed adaptive parameter control mechanism can yield better results than a static selection.