Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

We develop a hybrid algorithm using Genetic Algorithms (GA) and Simulated Annealing (SA) to solve multi-objective step function maximization problems. We then apply the algorithm to a specific economic problem which is taken out of the corporate governance literature.

This paper revisits the Kareken-Wallace model of exchange rate formation in a two-country overlapping generations world. Following the seminal paper by Arifovic (Journal of Political Economy, 104, 1996, 510 – 541) we investigate a dynamic version of the model in which agents? decision rules...

The paper presents a detailed documentation of the underlying concepts and methods of the Spatial Agent-based Competition Model (SpAbCoM). For instance, SpAbCoM is used to study firms' choices of spatial pricing policy (GRAUBNER et al., 2011a) or pricing and location under a framework of...

This contribution introduces a mixed-integer programming formulation for the multi-level, multi-machine proportional lot sizing and scheduling problem. It also presents a genetic algorithm to solve that problem. The efficiency of that algorithm is due to an encoding of solutions which uses a...

Cellular automata were used to model and to simulate phenomena in the area of physics, biology and medicine. In this paper it is now shown how the idea of cellular automata can be applied to optimization problems as well. As an example a cellular automaton is used as a basis for solving...

Summary This study presents a first comparative analysis of Lasso-type (Lasso, adaptive Lasso, elastic net) and heuristic subset selection methods. Although the Lasso has shown success in many situations, it has some limitations. In particular, inconsistent results are obtained for pairwise...

Prediction in financial domains is notoriously difficult for a number ofreasons. First, theories tend to be weak or non-existent, which makesproblem formulation open ended by forcing us to consider a large numberof independent variables and thereby increasing the dimensionality ofthe search...