  This paper addresses the development of a covariance matrix self-adaptation
evolution strategy (CMSA-ES) for solving optimization problems with linear
constraints. The proposed algorithm is referred to as Linear Constraint CMSA-ES
(lcCMSA-ES). It uses a specially built mutation operator together with repair
by projection to satisfy the constraints. The lcCMSA-ES evolves itself on a
linear manifold defined by the constraints. The objective function is only
evaluated at feasible search points (interior point method). This is a property
often required in application domains such as simulation optimization and
finite element methods. The algorithm is tested on a variety of different test
problems revealing considerable results.
