  This research explores substitution of the fittest (SF), a technique designed
to counteract the problem of disengagement in two-population competitive
coevolutionary genetic algorithms. SF is domain-independent and requires no
calibration. We first perform a controlled comparative evaluation of SF's
ability to maintain engagement and discover optimal solutions in a minimal toy
domain. Experimental results demonstrate that SF is able to maintain engagement
better than other techniques in the literature. We then address the more
complex real-world problem of evolving recommendations for health and
well-being. We introduce a coevolutionary extension of EvoRecSys, a previously
published evolutionary recommender system. We demonstrate that SF is able to
maintain engagement better than other techniques in the literature, and the
resultant recommendations using SF are higher quality and more diverse than
those produced by EvoRecSys.
