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

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2 Author(s)
Beyer, H.-G. ; Dept. of Comput. Sci., Vorarlberg Univ. of Appl. Sci., Dornbirn, Austria ; Finck, S.

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. While the feasible solution space is defined by the (probabilistic) simplex, the nonlinearity comes in by a cardinality constraint bounding the number of linear inequalities violated. This gives rise to a nonconvex optimization problem. The design is based on the CMSA-ES and relies on three specific techniques to fulfill the different constraints. The resulting algorithm is then thoroughly tested on a data set derived from time series data of the Dow Jones Index.

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Evolutionary Computation, IEEE Transactions on  (Volume:16 ,  Issue: 4 )