A new Probe Guided Mutation operator and its application for solving the cardinality constrained portfolio optimization problem
作者： K. Liagkouras , K. Metaxiotis
DOI: 10.1016/J.ESWA.2014.03.051
关键词: Mathematical optimization 、 Quadratic programming 、 Portfolio optimization problem 、 Mutation operator 、 Portfolio optimization 、 Evolutionary algorithm 、 Algorithm 、 Pareto principle 、 Efficient frontier 、 Mathematics 、 Multi-objective optimization
摘要: Abstract This paper revisits the classical Polynomial Mutation (PLM) operator and proposes a new probe guided version of PLM designed to be used in conjunction with Multiobjective Evolutionary Algorithms (MOEAs). The proposed Probe Guided (PGM) is validated by using data sets from six different stock markets. performance PGM assessed comparison one assistance Non-dominated Sorting Genetic Algorithm II (NSGAII) Strength Pareto 2 (SPEA2). evaluation based on three metrics, namely Hypervolume, Spread Epsilon indicator. experimental results reveal that outperforms confidence for all metrics when applied solution cardinality constrained portfolio optimization problem (CCPOP). We also calculate True Efficient Frontier (TEF) CCPOP formulating as Mixed Integer Quadratic Program (MIQP) we compare relevant approximate efficient frontiers are generated operator. confirm generates near optimal solutions lie very close or certain cases overlap TEF.
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