Evolving novel behaviors via natural selection (poster)
ALIFE Proceedings of the sixth international conference on Artificial life
A framework for sensor evolution in a population of Braitenberg vehicle-like agents (poster)
ALIFE Proceedings of the sixth international conference on Artificial life
Artificial Ethology
Artificial Life II
Social coordination without communication in multi-agent territory exploration tasks
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Adaptive algorithms for the dynamic distribution and parallel execution of agent-based models
Journal of Parallel and Distributed Computing - Special issue on parallel bioinspired algorithms
Investigating the Adaptiveness of Communication in Multi-Agent Behavior Coordination
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
MONEE: using parental investment to combine open-ended and task-driven evolution
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Right on the MONEE: combining task- and environment-driven evolution
Proceedings of the 15th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Evolutionary investigations are often very expensive in terms of the required computational resources and many general questions regarding the utility of a feature F of an agent (e.g., in competitive environments) or the likelihood of F evolving (or not evolving) are therefore typically difficult, if not practically impossible to answer. We propose and demonstrate in extensive simulations a methodology that allows us to answer such questions in setups where good predictors of performance in a task T are available. These predictors evaluate the performance of an agent kind A in a task T*, which can then transformed by including costs and additional factors to make predictions about the performance of A in T.