On the Design of Constraint Covariance Matrix Self-Adaptation Evolution Strategies Including a Cardinality Constraint


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.

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