Evolving mobile robots in simulated and real environments
Artificial Life
Evolving neural networks through augmenting topologies
Evolutionary Computation
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
A Normalized Levenshtein Distance Metric
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering several robot behaviors through speciation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
Generating diverse behaviors of evolutionary robots with speciation for theory of mind
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
| Hi-index | 0.00 |
This paper describes a speciation method that allows an evolutionary process to learn several robot behaviors using a single execution. Species are created in behavioral space in order to promote the discovery of different strategies that can solve the same navigation problem. Candidate neurocontrollers are grouped into species based on their corresponding behavior signature, which represents the traversed path of the robot within the environment.Behavior signatures are encoded using character strings and are compared using the string edit distance. The proposed approach is better suited for an evolutionary robotics problem than speciating in objective or topological space. Experimental comparison with the NEAT method confirms the usefulness of the proposal.