16,560 research outputs found
Data-efficient Neuroevolution with Kernel-Based Surrogate Models
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.Comment: In GECCO 201
Population Synthesis via k-Nearest Neighbor Crossover Kernel
The recent development of multi-agent simulations brings about a need for
population synthesis. It is a task of reconstructing the entire population from
a sampling survey of limited size (1% or so), supplying the initial conditions
from which simulations begin. This paper presents a new kernel density
estimator for this task. Our method is an analogue of the classical
Breiman-Meisel-Purcell estimator, but employs novel techniques that harness the
huge degree of freedom which is required to model high-dimensional nonlinearly
correlated datasets: the crossover kernel, the k-nearest neighbor restriction
of the kernel construction set and the bagging of kernels. The performance as a
statistical estimator is examined through real and synthetic datasets. We
provide an "optimization-free" parameter selection rule for our method, a
theory of how our method works and a computational cost analysis. To
demonstrate the usefulness as a population synthesizer, our method is applied
to a household synthesis task for an urban micro-simulator.Comment: 10 pages, 4 figures, IEEE International Conference on Data Mining
(ICDM) 201
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