TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Gaier, Adam
A1  - Asteroth, Alexander
A1  - Mouret, Jean-Baptiste
T1  - Data-efficient Neuroevolution with Kernel-Based Surrogate Models
T2  - GECCO ’18: Genetic and Evolutionary Computation Conference, July 15–19, 2018, Kyoto, Japan
N2  - Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms. Neuroevolution, however, has so far resisted the application of these techniques because it requires the surrogate model to make fitness predictions based on variable topologies, instead of a vector of parameters. Our main insight is that we can sidestep this problem by using kernel-based surrogate models, which require only the definition of a distance measure between individuals. Our second insight is that the well-established Neuroevolution of Augmenting Topologies (NEAT) algorithm provides a computationally efficient distance measure between dissimilar networks in the form of "compatibility distance", initially designed to maintain topological diversity. Combining these two ideas, we introduce a surrogate-assisted neuroevolution algorithm that combines NEAT and a surrogate model built using a compatibility distance kernel. We demonstrate the data-efficiency of this new algorithm on the low dimensional cart-pole swing-up problem, as well as the higher dimensional half-cheetah running task. In both tasks the surrogate-assisted variant achieves the same or better results with several times fewer function evaluations as the original NEAT.
KW  - Neuroevolution
KW  - Surrogate Modeling
KW  - NEAT
SN  - 978-1-4503-5618-3
SB  - 978-1-4503-5618-3
U6  - https://doi.org/10.1145/3205455.3205510
DO  - https://doi.org/10.1145/3205455.3205510
AX  - 1804.05364
SP  - 85
EP  - 92
PB  - ACM
CY  - New York, NY, USA
ER  -