We study an evolutionary algorithm used for optimizing in a chaotically changing dynamic environment. The corresponding chaotic non–stationary fitness landscape can be character...
To predict the 100 missing values from a time series of 5000 data points, given for the IJCNN 2004 time series prediction competition, recurrent neural networks (RNNs) are trained...
Xindi Cai, Nian Zhang, Ganesh K. Venayagamoorthy, ...
Abstract. We present a new method for proving lower bounds in evolutionary computation based on fitness-level arguments and an additional condition on transition probabilities bet...
Bayesian networks (BN) constitute a useful tool to model the joint distribution of a set of random variables of interest. To deal with the problem of learning sensible BN models fr...
Abstract. In this paper, we apply a competitive coevolutionary approach using loosely coupled genetic algorithms to a distributed optimization of the Rosenbrock's function. Th...
Franciszek Seredynski, Pascal Bouvry, Farhad Arbab