Selecting optimal investment portfolios becomes an NP-hard problem once realistic constraints, like limiting the number of assets held, are introduced, making exact solutions impractical at scale. This paper enhances multi-objective evolutionary algorithms with new solution representations, operators, and repair mechanisms tailored to asset-count-constrained portfolio problems, combined with improved mating strategies. Tested against traditional algorithms using established market indices, the method converges faster and finds better solutions without performance loss as market size grows. Applications include practical portfolio construction tools for asset managers and individual investors needing to balance diversification against transaction costs and monitoring overhead.
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