Helmut A. Mayer

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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)
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 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)
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)
The majority of work on artificial neural networks (ANNs) playing the game of Go focus on network architectures and training regimes to improve the quality of the neural player. A less investigated problem is the board representation conveying the information on the current state of the game to the network. Common approaches suggest a straight-forward(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)
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)
Contemplating the development of the field of evolutionary computation (EC), where most basic concepts are borrowed from nature, it is remarkable that multi– chromosomal representations present in all complex organisms have rarely been studied in the artificial domain. Evidently, the addition of such an additional layer of genetic code must prove to possess(More)