Jan Drchal

My photographJan Drchal works as a postgraduate student and researcher at the Department of Computer Science and Engineering. He got his master degree form FEE-CTU in 2006. He joined the group in 2003. His research focuses on recurrent artificial neural networks.



  • Drchal, J. and Kapraľ, O. and Koutník, J. and Šnorek, M.: Combining Multiple Inputs in HyperNEAT Mobile Agent Controller. vol. 2 nr. , p. 775-783, Springer, Berlin, 2009. ISSN 0302-9743 BibTex, PDF

    In this paper we present neuro-evolution of neural network controllers for mobile agents in a simulated environment. The controller is obtained through evolution of hypercube encoded weights of recurrent neural networks (HyperNEAT). The simulated agent’s goal is to find a target in a shortest time interval. The generated neural network processes three different inputs – surface quality, obstacles and distance to the target. A behavior emerged in agents features ability of driving on roads, obstacle avoidance and provides an efficient way of the target search.

  • Drchal, J. and Koutník, J. and Šnorek, M.: HyperNEAT Controlled Robots Learn How to Drive on Roads in Simulated Environment. In: 2009 IEEE Congress on Evolutionary Computation, p. 6, Research Publishing Services, Singapore, 2009. ISBN 978-1-4244-2959-2 BibTex, PDF

    In this paper we describe simulation of autonomous robots controlled by recurrent neural networks, which are evolved through indirect encoding using HyperNEAT algorithm. The robots utilize 180 degree wide sensor array. Thanks to the scalability of the neural network generated by HyperNEAT, the sensor array can have various resolution. This would allow to use camera as an input for neural network controller used in real robot. The robots were simulated using software simulation environment. In the experiments the robots were trained to drive with imaximum average speed. Such fitness forces them to learn how to drive on roads and avoid collisions. Evolved neural networks show excellent scalability. Scaling of the sensory input breaks performance of the robots, which should be gained back with re-training of the robot with a different sensory input resolution.

  • Drchal J., Šnorek M.: Diversity visualization in evolutionary algorithms. In: Proceedings of 41th Spring International Conference MOSIS 07, Modelling and Simulation of Systems, p. 77--84, Ostrava: MARQ, 2007. ISBN 978-80-86840-30-7 BibTex, PDF

    Evolutionary Algorithms (EAs) are well-known nature-inspired optimization methods. Diversity is an essenial aspect of each EA. It describes the variability of organisms in population. The lack of diversity is common problem - diversity should be preserved in order to evade local extremes (premature convergence). Niching algorithms are modifications of classical EAs. Niching is based on dividing the population into separate subpopulations - it spreads the organisms effectively all over the search space and hence making the overall population diverse. Using niching methods also requires setting of their parameters, which can be very difficult. This paper presents a novel way of diversity visualization based on physical system simulation. This visualization is helpful when designing and tuning niching algorithms but it has also other uses. The visualization will be presented on NEAT - the evolutionary algorithm which optimizes both the topology and the parameters of neural networks.

  • Drchal J., Kordík P., Koutník J.: Visualization of Diversity in Computational Intelligence Methods. In: Proceedings of 2nd ISGI, International CODATA Symposium on Generalization of Information, p. 20-34, CODATA Germany, 2007. ISBN 978-3-00-022382-2 BibTex
  • Drchal J.: Evolution of Recurrent Neural Networks. At: Czech Technical University in Prague, 2006 BibTex, PDF

    This diploma thesis deals with NEAT (NeuroEvolution Of Augmenting Topologies) - it is a state of the art evolutionary system for optimizing topology and parameters of recurrent neural networks. This area of research attracts considerable interest at present. In order to experiment with the algorithm, system is implemented in Java programming language. Then experiments to reproduce previously published results are performed. Work continues with the proposal of a modified system DC NEAT which is then compared to original NEAT. 2-D and 3-D visualizations of recurrent neural networks are proposed and implemented.

  • Drchal J., Šnorek M., Kordík P.: Maintaining Diversity in Population of Evolved Models. In: Proceedings of 40th Spring International Conference MOSIS 06, Modelling and Simulation of Systems, Ostrava: MARQ, 2006. ISBN 80-86840-21-2 BibTex, PDF

    This paper deals with creation of models by means of evolutionary algorithms, particularly with maintaining diversity of population using niching methods. Niching algorithms are known for their ability to search for more optima simultaneously. This is done by splitting the population of models into separate species. Species protect promising but yet not fully developed models. Search for more optima at the same time helps to avoid a premature convergence and therefore deals effectively with local optima. Efficiency of two different niching methods is compared on NEAT applied to the neuro-evolution of models.

  • Kučerová A., Drchal J., Němeček J., Lepš M.: Optimizing of Neural Networks Using a Genetic Algorithm. In: Proceedings of WORKSHOP 2003, p. 316-317, Czech Technical University in Prague, 2003. ISBN 80-01-02708-2 BibTex
  • Drchal J., Kučerová A., Němeček J.: Using a Genetic Algorithm for Optimizing Synaptic Weights of Neural Networks. CTU Report vol. 1 nr. 7, p. 161-172, , 2003. ISSN BibTex, PDF

    This paper describes how the SADE genetic algorithm, we developed, could be used for training neural networks. We first make a comparison of the SADE with the traditional backpropagation method and then we demonstrate one of its applications in civil engineering.

  • Drchal J., Kučerová A.: On Using Evolutionary Computational Methods for Layered Neural Network Training. In: Proceedings of the 6th International Student Conference on Electrical Engineering, POSTER 2002, , 2002. ISBN BibTex
  • Drchal J., Kučerová A., Němeček J.: Optimizing Synaptic Weights of Neural Networks. In: Proceedings of the Third International Conference on Engineering Computational Technology, Civil-Comp Press, Stirling, United Kingdom, 2002. ISBN BibTex