We consider the issue of intensification/diversification balance in the context of a memetic algorithm for the multiobjective optimization of investment portfolios with cardinality constraints. We approach this issue in this work by considering the selective application of knowledge-augmented operators (local search and a memory of elite solutions) based on the search epoch in which the algorithm finds itself, hence alternating between unbiased search (guided uniquely by the built-in search mechanics of the algorithm) and focused search (intensified by the use of the problem-aware operators). These operators exploit Sharpe index (a measure of the relationship between return and risk) as a source of problem knowledge. We have conducted a sensibility analysis to determine in which phases of the search the application of these operators leads to better results. Our findings indicate that the resulting algorithm is quite robust in terms of parameterization from the point of view of this problem-specific indicator. Furthermore, it is shown that not only can other non-memetic counterparts be outperformed, but that there is a range of parameters in which the MA is also competitive when not better in terms of standard multiobjective performance indicators.

翻译：本文研究了在带基数约束的投资组合多目标优化模因算法中，强化与多样化之间的平衡问题。我们通过基于算法所处搜索周期选择性应用知识增强算子（局部搜索与精英解记忆机制）来解决该问题，从而在无偏搜索（仅由算法内置搜索机制引导）与聚焦搜索（通过问题感知算子强化）之间进行交替。这些算子利用夏普指数（衡量收益与风险关系的指标）作为问题知识的来源。我们通过敏感性分析确定了在搜索的哪些阶段应用这些算子能够获得更优结果。研究结果表明，从该问题特异性指标的角度来看，所得算法在参数化方面表现出较强的鲁棒性。此外，实验不仅证明该算法能够超越非模因算法，还表明存在一系列参数范围，使得模因算法在标准多目标性能指标上具有竞争力甚至更优表现。