Helmut A. Mayer

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The prediction of future values of a time series generated by a chaotic dynamical system is an extremely challenging task. Amongst several non-linear models employed for the prediction of chaotic time series artificial neural networks (ANNs) have gained major attention in the past decade. One widely recognized aspect of ANN design in order to achieve(More)
We investigate evolutionary approaches to generate well-performing strategies for the iterated prisoner's dilemma (IPD) with different history lengths in static and cultural environments. The length of the history determines the number of the most recent moves of both players taken into account for the current move decision. The static environment(More)
Artiicial neural networks (ANNs) have shown to perform satisfactorily for pattern recognition tasks. It has also been shown that ANNs are superior to some of the classical statistical methods in pattern classiication, but little is known how to design the ANN. A genetic algorithm (GA) based method can be used to determine the ANN architecture for a speciic(More)
We present experiments investigating the use of multi–chromosomal representations in evolutionary algorithms. Specifically, the conventional representation of parameters on a single chromosome is compared to a genotype encoding with multiple chromosomes on a set of test functions. In this context we present chromosome shuffling, a genetic operator(More)
We present experiments (co)evolving Go players based on artificial neural networks (ANNs) for a 5x5 board. ANN structure and weights are encoded in multi–chromosomal genotypes. In evolutionary scenarios a population of generalized multi–layer perceptrons (GMLPs) has to compete with a single Go program from a set of three players of different quality. Two(More)
A main criterion for the accuracy of solutions of an Artiicial Neural Network (ANN) for classiica-tion tasks is the architecture. In order to nd problem-adapted topologies of ANNs, we adopted the evolutionary approach to ANN design by employing a Genetic Algorithm (GA) to evolve ANNs which are represented using a direct encoding method. As ANNs of low(More)
In this article we present work on chromosome structures for genetic algorithms (GAs) based on biological principles. Mainly, the influence of noncoding segments on GA behavior and performance is investigated. We compare representations with noncoding sequences at predefined, fixed locations with "junk" code induced by the use of promoter/terminator(More)
Artiicial Neural Networks (ANN) have gained increasing popularity as an alternative to statistical methods for classiication of Remote Sensing Data. Their superiority to some of the classical statistical methods has been shown in the literature. Therefore, ANNs are commonly used for segmentation and classiication purposes. We address the problem of(More)
A main criterion for the accuracy of solutions of Artiicial Neural Networks (ANNs) for classii-cation tasks is the architecture. In order to nd problem-adapted topologies of ANNs, we adopted the evolutionary approach to ANN design by employing a Genetic Algorithm (GA) to evolve ANNs which are represented using a direct encoding method. The role of tness(More)