All Outputs (136)

Evolving software test data: GA's learn self expression (1996)
Book Chapter
Smith, J., & Fogarty, T. (1996). Evolving software test data: GA's learn self expression. In T. Fogarty (Ed.), Evolutionary Computing (137-146). Springer

Visualising state space representations of LSTM networks
Presentation / Conference
Smith, E. M., Smith, J., Legg, P., & Francis, S. Visualising state space representations of LSTM networks. Presented at Workshop on Visualization for AI Explainability, Berlin, Germany

Long Short-Term Memory (LSTM) networks have proven to be one of the most effective models for making predictions on sequence-based tasks. These models work by capturing, remembering, and forgetting information relevant to their future predictions. Th... Read More about Visualising state space representations of LSTM networks.

Making early predictions of the accuracy of machine learning applications
Working Paper
Smith, J., Caleb-Solly, P., Tahir, M. A., Sannen, D., & van-Brussel, H. (2012). Making early predictions of the accuracy of machine learning applications

Teaching artificial intelligence with pac-man
Working Paper
Smith, J. (2009). Teaching artificial intelligence with pac-man

Credit assignment in adaptive memetic algorithms
Presentation / Conference
Smith, J. Credit assignment in adaptive memetic algorithms. Paper presented at Proceedings of Gecco, the ACM-SIGEVO Conference on Evolutionary Computation, Springer, Berlin, Heidelberg, New York

Stop wasting time: On predicting the success or failure of learning for industrial applications
Presentation / Conference
Smith, J., & Tahir, M. Stop wasting time: On predicting the success or failure of learning for industrial applications. Paper presented at Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'08), Springer, Berlin, Heidelberg, New York

Incorporation of adaptive mutation based on subjective evaluation in an interactive evolution strategy
Presentation / Conference
Caleb-Solly, P., & Smith, J. Incorporation of adaptive mutation based on subjective evaluation in an interactive evolution strategy. Paper presented at Proceedings of the IEEE Congress on Evolutionary Computation, Piscataway, NJ

What have gene libraries done for AIS?
Presentation / Conference
Cayzer, S., Smith, J., Marshall, J., & Kovacs, T. What have gene libraries done for AIS?. Paper presented at Proceedings of ICARIS 2005: 4th International Conference on Artificial Immune Systems, Springer, Berlin, Heidelberg, New York

Interactive evolutionary strategy based discovery of image segmentation parameters
Presentation / Conference
Caleb-Solly, P., & Smith, J. Interactive evolutionary strategy based discovery of image segmentation parameters. Paper presented at Adaptive Computing in Design and Manufacture VI, Springer, Berlin, Heidelberg, New York

Study of fitness landscapes for the HP model of protein structure prediction
Presentation / Conference
Duarte-Flores, S., & Smith, J. Study of fitness landscapes for the HP model of protein structure prediction. Paper presented at 2003 Congress on Evolutionary Computation (CEC'2003), Piscataway, NJ

Co-evolving memetic algorithms: A learning approach to robust scalable optimisation
Presentation / Conference
Smith, J. Co-evolving memetic algorithms: A learning approach to robust scalable optimisation. Paper presented at 2003 Congress on Evolutionary Computation (CEC'2003), Piscataway, NJ

Parameter perturbation mechanisms in binary coded gas with self-adaptive mutation
Presentation / Conference
Smith, J. Parameter perturbation mechanisms in binary coded gas with self-adaptive mutation. Paper presented at Foundations of Genetic Algorithms 7, San Francisco, USA

Emergence of profitable search strategies based on a simple inheritance mechanism
Presentation / Conference
Krasnogor, N., & Smith, J. Emergence of profitable search strategies based on a simple inheritance mechanism. Paper presented at Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), Morgan Kaufmann, San Francisco, USA

Adaptively parameterised evolutionary systems: Self adaptive recombination and mutation in a genetic algorithm
Presentation / Conference
Smith, J., & Fogarty, T. Adaptively parameterised evolutionary systems: Self adaptive recombination and mutation in a genetic algorithm. Paper presented at Proceedings of the 4th Conference on Parallel Problem Solving from Nature, Springer, Berlin, Heidelberg, New York

An adaptive poly-parental recombination strategy
Presentation / Conference
Smith, J., & Fogarty, T. An adaptive poly-parental recombination strategy. Paper presented at Evolutionary Computing 2, Springer, Berlin, Heidelberg, New York

Genetic feature selection for clustering and classification
Presentation / Conference
Smith, J., Fogarty, T., & Johnson, I. Genetic feature selection for clustering and classification. Paper presented at Proceedings of the IEE Colloquium on Genetic Algorithms in Image Processing and Vision