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We introduce Embodied Evolution (EE) as a new methodology for evolutionary robotics (ER). EE uses a population of physical robots that autonomously reproduce with one another while situated in their task environment. This constitutes a fully distributed evolutionary algorithm embodied in physical robots. Several issues identified by researchers in the(More)
Herbert A. Simon's characterization of modularity in dynamical systems describes subsystems as having dynamics that are approximately independent of those of other subsystems (in the short term). This fits with the general intuition that modules must, by definition, be approximately independent. In the evolution of complex systems, such modularity may(More)
We introduce Embodied Evolution (EE) as a methodology for the automatic design of robotic controllers. EE is an evolutionary robotics (ER) technique that avoids the pitfalls of the simulate-and-transfer method, allows the speed-up of evaluation time by utilizing parallelism, and is particularly suited to future work on multi-agent behaviors. In EE, an(More)
One of the most controversial yet enduring hypotheses about what genetic algorithms (GAs) are good for concerns the idea that GAs process building-blocks. More specifically, it has been suggested that crossover in GAs can assemble short low-order schemata of above average fitness (building blocks) to create higher-order higher-fitness schemata. However,(More)
One common characterization of how simple hill-climbing optimization methods can fail is that they become trapped in local op-tima-a state where no small modiication of the current best solution will produce a solution that is better. This measure of`better' depends on the performance of the solution with respect to the single objective b e-ing optimized.(More)