  Many systems require optimisation over multiple objectives, where objectives
are characteristics of the system such as energy consumed or increase in time
to perform the work. Optimisation is performed by selecting the `best' set of
input parameters to elicit the desired objectives. However, the parameter
search space can often be far larger than can be searched in a reasonable time.
Additionally, the objectives are often mutually exclusive -- leading to a
decision being made as to which objective is more important or optimising over
a combination of the objectives. This work is an application of a Genetic
Algorithm to identify the Pareto frontier for finding the optimal parameter
sets for all combinations of objectives. A Pareto frontier can be used to
identify the sets of optimal parameters for which each is the `best' for a
given combination of objectives -- thus allowing decisions to be made with full
knowledge. We demonstrate this approach for the HTC-Sim simulation system in
the case where a Reinforcement Learning scheduler is tuned for the two
objectives of energy consumption and task overhead. Demonstrating that this
approach can reduce the energy consumed by ~36% over previously published work
without significantly increasing the overhead.
