While common machine learning algorithms focus on minimizing the mean-square errors of model fit, we show that genetic programming, GP, is well-suited to maximize an economic objective, the Sharpe ratio of the usual spread portfolio in the cross-section of expected stock returns. In contrast to...

Prediction helps organizations in their decision-making activities for the improvement of the different functions, services, and even their income. The C4.5 algorithm is the most commonly used prediction algorithm having difficulty in choosing attributes that lead to low prediction accuracy....

Estimating parameters and establishing high-accuracy and high-reliability models of photovoltaic (PV) modules by using the actual current-voltage data is important to simulate, model, and optimize the PV systems. Several meta-heuristic optimization techniques have been developed to estimate the...

We develop a novel Mean-Max Drawdown portfolio optimization approach using buy-and-hold portfolios. The optimization is performed utilizing a multi-objective evolutionary algorithm on a sample of S&P 100 constituents. Our optimization procedure provides portfolios with better Mean-Max Drawdown...

This work presents a novel evolutionary computation-based Padé approximation (EPA) scheme for constructing a closed-form approximate solution of a nonlinear dynamical model of Covid-19 disease with a crowding effect that is a growing trend in epidemiological modeling. In the proposed framework...

Purpose: The purpose of this paper is to describe the implementation of a Fleet Management System (FMS) that plans and controls the execution of logistics tasks by a set of mobile robots in a real-world hospital environment. The FMS is developed upon an architecture that hosts a routing engine,...

This paper is focused on the concise review of the specific applications of genetic algorithms in forecasting commodity prices. Genetic algorithms seem relevant in this field for many reasons. For instance, they lack the necessity to assume a certain statistical distribution, and they are...

It has been shown that evolutionary algorithms are able to construct suitable search strategies for classes of Constraint Satisfaction Problems (CSPs) in Constraint Programming. This paper is an explanation of the use of multi-objective optimisation in contrast to simple additive weighting...

Automatic design of stochastic local search algorithms has been shown to be very effective in generating algorithms for the permutation flowshop problem for the most studied objectives including makespan, flowtime and total tardiness. The automatic design system uses a configuration tool to...