Mixed-Integer Non-Linear Programming (MINLP) is not rare in real-world applications such as portfolio invest-ment. It has brought great challenges to optimization methods due to the complicated search space that has b...
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Mixed-Integer Non-Linear Programming (MINLP) is not rare in real-world applications such as portfolio invest-ment. It has brought great challenges to optimization methods due to the complicated search space that has both continuous and discrete variables. This paper considers the multi-objectiveconstrained portfolio optimization problems that can be formulated as MINLP problems. Since each continuous variable is dependent to a discrete variable, we propose a Compressed Coding Scheme (CCS), which encodes the dependent variables into a contin-uous one. In this manner, we can reuse some existing search operators and the dependence among variables will be utilized while the algorithm is optimizing the compressed variables. CCS actually bridges the gap between the portfolio optimization problems and the existing optimizers, such as multi-objective Evolutionary Algorithms (MOEAs). The new approach is applied to two benchmark suites, involving the number of assets from 31 to 2235. The experimental results indicate that CCS is not only efficient but also robust for dealing with the multi-objectiveconstrained portfolio optimization problems.
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