  The performance of evolutionary algorithms can be heavily undermined when
constraints limit the feasible areas of the search space. For instance, while
Covariance Matrix Adaptation Evolution Strategy is one of the most efficient
algorithms for unconstrained optimization problems, it cannot be readily
applied to constrained ones. Here, we used concepts from Memetic Computing,
i.e. the harmonious combination of multiple units of algorithmic information,
and Viability Evolution, an alternative abstraction of artificial evolution, to
devise a novel approach for solving optimization problems with inequality
constraints. Viability Evolution emphasizes elimination of solutions not
satisfying viability criteria, defined as boundaries on objectives and
constraints. These boundaries are adapted during the search to drive a
population of local search units, based on Covariance Matrix Adaptation
Evolution Strategy, towards feasible regions. These units can be recombined by
means of Differential Evolution operators. Of crucial importance for the
performance of our method, an adaptive scheduler toggles between exploitation
and exploration by selecting to advance one of the local search units and/or
recombine them. The proposed algorithm can outperform several state-of-the-art
methods on a diverse set of benchmark and engineering problems, both for
quality of solutions and computational resources needed.
