16 citations · 18 across the 6 of their papers we have counts for
6 papers
Finding Near-Optimal Portfolios With Quality-Diversity
Bruno Gašperov, Marko Đurasević, Domagoj Jakobovic
The majority of standard approaches to financial portfolio optimization (PO) are based on the mean-variance (MV) framework. Given a risk aversion coefficient, the MV procedure yiel…
A New Angle: On Evolving Rotation Symmetric Boolean Functions
Claude Carlet, Marko Ðurasevic, Bruno Gašperov +3
Rotation symmetric Boolean functions represent an interesting class of Boolean functions as they are relatively rare compared to general Boolean functions. At the same time, the fu…
Deep Reinforcement Learning for Robust Goal-Based Wealth Management
Tessa Bauman, Bruno Gašperov, Stjepan Begušić +1
Goal-based investing is an approach to wealth management that prioritizes achieving specific financial goals. It is naturally formulated as a sequential decision-making problem as…
On Evolvability and Behavior Landscapes in Neuroevolutionary Divergent Search
Bruno Gašperov, Marko Đurasević
Evolvability refers to the ability of an individual genotype (solution) to produce offspring with mutually diverse phenotypes. Recent research has demonstrated that divergent searc…
A Search for Nonlinear Balanced Boolean Functions by Leveraging Phenotypic Properties
Bruno Gašperov, Marko Đurasević, Domagoj Jakobović
In this paper, we consider the problem of finding perfectly balanced Boolean functions with high non-linearity values. Such functions have extensive applications in domains such as…
Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model
Bruno Gašperov, Zvonko Kostanjčar
The stochastic control problem of optimal market making is among the central problems in quantitative finance. In this paper, a deep reinforcement learning-based controller is trai…