| 2018 | GECCO | XCSR based on compressed input by deep neural network for high dimensional data. | Kazuma Matsumoto, Ryo Takano, Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2018 | GECCO | XCS-CR: determining accuracy of classifier by its collective reward in action set toward environment with action noise. | Takato Tatsumi, Tim Kovacs, Keiki Takadama |
| 2018 | GECCO | Generalizing rules by random forest-based learning classifier systems for high-dimensional data mining. | Fumito Uwano, Koji Dobashi, Keiki Takadama, Tim Kovacs |
| 2018 | GECCO | Classifier generalization for comprehensive classifiers subsumption in XCS. | Caili Zhang, Takato Tatsumi, Hiyoyuki Sato, Tim Kovacs, Keiki Takadama |
| 2017 | CEC | Applying variance-based Learning Classifier System without Convergence of Reward Estimation into various Reward distribution. | Takato Tatsumi, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2016 | CEC | XCS-DH: Minimal default hierarchies in XCS. | Tim Kovacs, Simon Rawles, Larry Bull, Masaya Nakata, Keiki Takadama |
| 2016 | CEC | Learning classifier system with deep autoencoder. | Kazuma Matsumoto, Yusuke Tajima, Rei Saito, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2016 | GECCO | Variance-based Learning Classifier System without Convergence of Reward Estimation. | Takato Tatsumi, Takahiro Komine, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2016 | HCI | Preventing Incorrect Opinion Sharing with Weighted Relationship Among Agents. | Rei Saito, Masaya Nakata, Hiroyuki Sato, Tim Kovacs, Keiki Takadama |
| 2015 | CEC | How should Learning Classifier Systems cover a state-action space? | Masaya Nakata, Pier Luca Lanzi, Tim Kovacs, Will Neil Browne, Keiki Takadama |
| 2015 | CEC | TP-XCS: An XCS classifier system with fixed-length memory for reinforcement learning. | Tom Pickering, Tim Kovacs |
| 2014 | GECCO | A modified XCS classifier system for sequence labeling. | Masaya Nakata, Tim Kovacs, Keiki Takadama |
| 2014 | GECCO | Complete action map or best action map in accuracy-based reinforcement learning classifier systems. | Masaya Nakata, Pier Luca Lanzi, Tim Kovacs, Keiki Takadama |
| 2014 | PPSN | Messy Coding in the XCS Classifier System for Sequence Labeling. | Masaya Nakata, Tim Kovacs, Keiki Takadama |
| 2013 | GECCO | Analysis of the niche genetic algorithm in learning classifier systems. | Tim Kovacs, Robin Tindale |
| 2011 | GECCO | Online, GA based mixture of experts: a probabilistic model of ucs. | Narayanan Unny Edakunni, Gavin Brown, Tim Kovacs |
| 2011 | GECCO | Accuracy exponentiation in UCS and its effect on voting margins. | Tim Kovacs, Narayanan Unny Edakunni, Gavin Brown |
| 2011 | UAI | Boosting as a Product of Experts. | Narayanan Unny Edakunni, Gary Brown, Tim Kovacs |
| 2009 | GECCO | Modeling UCS as a mixture of experts. | Narayanan Unny Edakunni, Tim Kovacs, Gavin Brown, James A. R. Marshall |
| 2007 | GECCO | UCSpv: principled voting in UCS rule populations. | Gavin Brown, Tim Kovacs, James A. R. Marshall |
| 2007 | GECCO | Toward a better understanding of rule initialisation and deletion. | Tim Kovacs, Larry Bull |
| 2007 | GECCO | Bayesian estimation of rule accuracy in UCS. | James A. R. Marshall, Gavin Brown, Tim Kovacs |
| 2006 | GECCO | A representational ecology for learning classifier systems. | James A. R. Marshall, Tim Kovacs |
| 2005 | GECCO | On the contribution of gene libraries to artificial immune systems. | Peter Spellward, Tim Kovacs |
| 2004 | GECCO | High Classification Accuracy Does Not Imply Effective Genetic Search. | Tim Kovacs, Manfred Kerber |
| 2002 | CEC | Performance and population state metrics for rule-based learning systems. | Tim Kovacs |
| 2001 | CEC | What should a classifier system learn? | Tim Kovacs |
| 2000 | FOGA | Towards a Theory of Strong Overgeneral Classifiers. | Tim Kovacs |