A CMA-ES-based 2-stage memetic framework for solving constrained optimization problems
Constraint optimization problems play a crucial role in many application domains, ranging from engineering design to finance and logistics. Specific techniques are therefore needed to handle complex fitness landscapes characterized by multiple constraints. In the last decades, a number of novel meta-heuristics have been applied to constraint optimization. Among these, the Covariance Matrix Adaptation Evolution
Vinícius Veloso de Melo is an Assistant Professor of Computer Science at