We study the complexity of combinatorial problems that consist of competing infeasibility and optimization components. In particular, we investigate the complexity of the connectio...
Jon Conrad, Carla P. Gomes, Willem Jan van Hoeve, ...
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...
In this paper, we consider each neural network as a point in a multi-dimensional problem space and suggest a crossover that locates the central point of a number of neural networks...
Abstract. The resolution of combinatorial optimization problems can greatly benefit from the parallel and distributed processing which is characteristic of neural network paradigm...
Abstract. A new model for evolving crossover operators for evolutionary function optimization is proposed in this paper. The model is a hybrid technique that combines a Genetic Pro...