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A new learning algorithm for multilayer 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)

- Heinrich Braun
- PPSN
- 1990

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)

- Heinrich Braun, Peter Zagorski
- PPSN
- 1994

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)

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

- Heinrich Braun, Peter Zagorski
- International Conference on Evolutionary…
- 1994

- Heinrich Braun
- 2007

As an introduction to heuristics based on the evolution as a paradigm, we give an overview over the most well known types: genetic algorithms, genetic programming, evolution strategies and evolutionary programming. Since the neural network paradigm seems to be a most promising approach to implement the intelligence of an agent (i.e. its control strategy),… (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)