Abstract. This paper introduces a framework based on multi-agent system for solving problems of combinatorial optimization. The framework allows running various metaheuristic algor...
Learning to solve small instances of a problem should help in solving large instances. Unfortunately, most neural network architectures do not exhibit this form of scalability. Our...
Abstract. In this paper we present a heuristic based on dynamic approximations for improving the well-known Schnorr-Euchner lattice basis reduction algorithm. In particular, the ne...
Regularization techniques have been in use in signal recovery for over four decades. In this paper, we propose a new, synthetic approach to the study of regularization methods in ...
Cooperative coevolution has proven to be a promising technique for solving complex combinatorial optimization problems. In this paper, we present four different strategies which i...
Leonardo Vanneschi, Giancarlo Mauri, Andrea Valsec...