Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Intelligence without representation
Artificial Intelligence
Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
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
Parisian camera placement for vision metrology
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
A Normalized Levenshtein Distance Metric
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Evolving mobile robots in simulated and real environments
Artificial Life
Speciation as automatic categorical modularization
IEEE Transactions on Evolutionary Computation
Planning multiple paths with evolutionary speciation
IEEE Transactions on Evolutionary Computation
Behavior-based speciation for evolutionary robotics
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Unified Behavior Framework for Reactive Robot Control
Journal of Intelligent and Robotic Systems
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Sustaining behavioral diversity in NEAT
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Why and how to measure exploration in behavioral space
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Picbreeder: A case study in collaborative evolutionary exploration of design space
Evolutionary Computation
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
Searching for novel classifiers
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Searching for novel clustering programs
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A behavior-based analysis of modal problems
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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This contribution studies speciation from the standpoint of evolutionary robotics (ER). A common approach to ER is to design a robot's control system using neuro-evolution during training. An extension to this methodology is presented here, where speciation is incorporated to the evolution process in order to obtain a varied set of solutions for a robotics problem using a single algorithmic run. Although speciation is common in evolutionary computation, it has been less explored in behavior-based robotics. When employed, speciation usually relies on a distance measure that allows different individuals to be compared. The distance measure is normally computed in objective or phenotypic space. However, the speciation process presented here is intended to produce several distinct robot behaviors; hence, speciation is sought in behavioral space. Thence, individual neurocontrollers are described using behavior signatures, which represent the traversed path of the robot within the training environment and are encoded using a character string. With this representation, behavior signatures are compared using the normalized Levenshtein distance metric (N-GLD). Results indicate that speciation in behavioral space does indeed allow the ER system to obtain several navigation strategies for a common experimental setup. This is illustrated by comparing the best individual from each species with those obtained using the Neuro-Evolution of Augmenting Topologies (NEAT) method which speciates neural networks in topological space.