Epoch-based Application of Problem-Aware Operators in a Multiobjective Memetic Algorithm for Portfolio Optimization

TL;DR

This paper introduces an epoch-based approach to applying problem-aware operators in a multiobjective memetic algorithm for portfolio optimization, balancing intensification and diversification using Sharpe index insights.

Contribution

It proposes a novel epoch-based method for selectively applying knowledge-augmented operators based on search phase, improving robustness and performance in portfolio optimization.

Findings

The algorithm outperforms non-memetic counterparts.

It is robust to parameter variations.

It achieves competitive results with standard indicators.

Abstract

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.