Evolving agent behavior in multiobjective domains using fitness-based shaping

Multiobjective evolutionary algorithms have long been applied to engineering problems. Lately they have also been used to evolve behaviors for intelligent agents. In such applications, it is often necessary to "shape" the behavior via increasingly difficult tasks. Such shaping requires extensive domain knowledge. An alternative is fitness-based shaping through changing selection pressures, which requires little to no domain knowledge. Two such methods are evaluated in this paper. The first approach, Targeting Unachieved Goals, dynamically chooses when an objective should be used for selection based on how well the population is performing in that objective. The second method, Behavioral Diversity, adds a behavioral diversity objective to the objective set. These approaches are implemented in the popular multiobjective evolutionary algorithm NSGA-II and evaluated in a multiobjective battle domain. Both methods outperform plain NSGA-II in evolution time and final performance, ...

• Type: Conference
• Year: 2010
• Where: GECCO
• Authors: Jacob Schrum, Risto Miikkulainen
• Keywords: Domain Knowledge, GECCO 2010, Multiobjective Battle Domain, Multiobjective Evolutionary Algorithm, Optimization