44 research outputs found
ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution
We consider the problem of efficient blackbox optimization over a large
hybrid search space, consisting of a mixture of a high dimensional continuous
space and a complex combinatorial space. Such examples arise commonly in
evolutionary computation, but also more recently, neuroevolution and
architecture search for Reinforcement Learning (RL) policies. Unfortunately
however, previous mutation-based approaches suffer in high dimensional
continuous spaces both theoretically and practically. We thus instead propose
ES-ENAS, a simple joint optimization procedure by combining Evolutionary
Strategies (ES) and combinatorial optimization techniques in a highly scalable
and intuitive way, inspired by the one-shot or supernet paradigm introduced in
Efficient Neural Architecture Search (ENAS). Through this relatively simple
marriage between two different lines of research, we are able to gain the best
of both worlds, and empirically demonstrate our approach by optimizing BBOB
functions over hybrid spaces as well as combinatorial neural network
architectures via edge pruning and quantization on popular RL benchmarks. Due
to the modularity of the algorithm, we also are able incorporate a wide variety
of popular techniques ranging from use of different continuous and
combinatorial optimizers, as well as constrained optimization.Comment: 22 pages. See
https://github.com/google-research/google-research/tree/master/es_enas for
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