On the Design of Constraint Covariance Matrix Self-Adaptation Evolution Strategies Including a Cardinality Constraint PROJECT TITLE :On the Design of Constraint Covariance Matrix Self-Adaptation Evolution Strategies Including a Cardinality ConstraintABSTRACT :This paper describes the algorithm's engineering of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving a mixed linear/nonlinear constrained optimization problem arising in portfolio optimization. Whereas the feasible answer area is defined by the (probabilistic) simplex, the nonlinearity comes in by a cardinality constraint bounding the quantity of linear inequalities violated. This offers rise to a nonconvex optimization drawback. The style is predicated on the CMSA-ES and relies on 3 specific techniques to fulfill the various constraints. The ensuing algorithm is then totally tested on a data set derived from time series data of the Dow Jones Index. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Promoting Creative Design in Interactive Evolutionary Computation Evolutionary Design of Both Topologies and Parameters of a Hybrid Dynamical System