Multiobjective Optimization
Instead of finding a single optimal solution to any given problem, multiobjective methods aim at finding a Pareto-front, which represents all of the trade-offs between objectives within the domain. A human decision maker can then decide which of the available trade-offs works best. Our work in this area focuses on generating multi-modal behavior, as well as maintaining diversity using multiobjective approaches.
Evolving Multimodal Behavior With Modular Neural Networks in Ms. Pac-Man Jacob Schrum and Risto Miikkulainen In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2014), 325--332,... 2014

Evolving Multimodal Behavior Through Modular Multiobjective Neuroevolution Jacob Schrum PhD Thesis, The University of Texas at Austin, Austin, TX 78712, May 2014. Tech Report TR-14-07. 2014

Effective Diversity Maintenance in Deceptive Domains Joel Lehman, Kenneth O. Stanley and Risto Miikkulainen To Appear In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) 2013,... 2013

Evolving Multimodal Networks for Multitask Games Jacob Schrum and Risto Miikkulainen IEEE Transactions on Computational Intelligence and AI in Games, 4(2):94--111, June 2012. IEE... 2012

Evolving Multimodal Networks for Multitask Games Jacob Schrum and Risto Miikkulainen In Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG 2011), 102... 2011

Evolving Agent Behavior In Multiobjective Domains Using Fitness-Based Shaping Jacob Schrum and Risto Miikkulainen In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2010), 439--446,... 2010

Evolving Multi-modal Behavior in NPCs Jacob Schrum and Risto Miikkulainen In IEEE Symposium on Computational Intelligence and Games (CIG 2009), 325--332, Milan, Italy,... 2009

Constructing Complex NPC Behavior via Multi-Objective Neuroevolution Jacob Schrum and Risto Miikkulainen In Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Confer... 2008

MARLEDA: Effective Distribution Estimation Through Markov Random Fields Matthew Alden PhD Thesis, Department of Computer Sciences, the University of Texas at Austin, Austin, Texas, 2007.... 2007

Jacob Schrum Ph.D. Alumni schrum2 [at] southwestern edu
MM-NEAT Modular Multiobjective NEAT is a software framework in Java that builds on the basic principles of 2014

UT^2: Winning Botprize 2012 Entry The Botprize Competition is an annual competition to program bots that appear human-l... 2012

BREVE Monsters BREVE is a system for designing Artificial Life simulations available at http://spiderlan... 2010