Optimising continuous microstructures: a comparison of gradient-based and stochastic methods

This work compares the use of a deterministic gradient based search with a stochastic genetic algorithm to optimise the geometry of a space frame structure. The goal is not necessarily to find a global optimum, but instead to derive a confident approximation of fitness to be used in a second optimisation of topology. The results show that although the genetic algorithm searches the space more broadly, and this space has several global optima, gradient descent achieves similar fitnesses with equal confidence. The gradient descent algorithm is advantageous however, as it is deterministic and results in a lower computational cost

Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm

This paper deals with online onboard behavior optimization for a autonomous mobile robot in the scope of the European FP7 Symbrion Project. The work presented here extends the (1+1)-online algorithm introduced in [4]. The (1+1)-online algorithm has a limitation regarding the ability to perform global search whenever a local optimum is reached. Our new implementation of the algorithm, termed (1+1)-restart-online algorithm, addresses this issue and has been successfully experimented using a Cortex M3 microcontroller connected to a realistic robot simulator as well as within an autonomous robot based on an Atmel ATmega128 microcontroller. Results from the experiments show that the new algorithm is able to escape local optima and to perform behavior optimization in a complete autonomous fashion. As a consequence, it is able to converge faster and provides a richer set of relevant controllers compared to the previous implementation