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A new learning algorithm for multi-layer feedforward networks, RPROP, is proposed. To overcome the inherent disadvantages of pure gradient-descent, RPROP performs a local adaptation of the weight-updates according to the behaviour of the errorfunction. In substantial difference to other adaptive techniques, the effect of the RPROP adaptation process is not… (More)

We present a genetic algorithm for solving the traveling salesman problem by genetic algorithms to optimality for traveling salesman problems with up to 442 cities. Mlihlenbein et al. [MGK 88], [MK 89] have proposed a genetic algorithm for the traveling salesman problem, which generates very good but not optimal solutions for traveling salesman problems… (More)

For many practical problem domains the use of neural networks has led to very satisfactory results. Nevertheless the choice of an appropriate, problem specific network architecture still remains a very poorly understood task. Given an actual problem, one can choose a few different architectures, train the chosen architectures a few times and finally select… (More)

- Heinrich Braun
- 1997

ENZO-M combines two successful search techniques using two diierent timescales: learning (gradient descent) for netuning of each oospring and evolution for coarse optimization steps of the network topology. Therefore, our evolutionary algorithm is a metaheuristic based on the best available local heuristic. Through training each oospring by fast gradient… (More)

In the last years we developed ENZO, an evolutionary neural network optimizer which surpasses other algorithms with regard to performance and scalability. In this study we compare ENZO to standard techniques for topology optimization: Optimal Brain Surgeon (OBS), Magnitude based Pruning (MbP), and to an improved algorithm deduced from OBS (unit-OBS).… (More)

For many practical problem domains the use of neural networks has led to very satisfactory results. Nevertheless the choice of an appropriate, problem specific network architecture still remains a very poorly understood task. Given an actual problem, one can choose a few different architectures, train the chosen architectures a few times and finally select… (More)

- Heinrich Braun
- 1990

In this paper we study the combination of two powerful approaches, evolutionary topology optimization (ENZO) and Tempoal Diierence Learning (TD()) which is up to our knowledge the rst time. Temporal Diierence Learning was proven to be a well suited technique for learning strategies for solving reinforcement problems based on neural network models , whereas… (More)