4,704 research outputs found
Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)
Recently, increasing works have proposed to drive evolutionary algorithms
using machine learning models. Usually, the performance of such model based
evolutionary algorithms is highly dependent on the training qualities of the
adopted models. Since it usually requires a certain amount of data (i.e. the
candidate solutions generated by the algorithms) for model training, the
performance deteriorates rapidly with the increase of the problem scales, due
to the curse of dimensionality. To address this issue, we propose a
multi-objective evolutionary algorithm driven by the generative adversarial
networks (GANs). At each generation of the proposed algorithm, the parent
solutions are first classified into real and fake samples to train the GANs;
then the offspring solutions are sampled by the trained GANs. Thanks to the
powerful generative ability of the GANs, our proposed algorithm is capable of
generating promising offspring solutions in high-dimensional decision space
with limited training data. The proposed algorithm is tested on 10 benchmark
problems with up to 200 decision variables. Experimental results on these test
problems demonstrate the effectiveness of the proposed algorithm
EvoX: A Distributed GPU-accelerated Library towards Scalable Evolutionary Computation
During the past decades, evolutionary computation (EC) has demonstrated
promising potential in solving various complex optimization problems of
relatively small scales. Nowadays, however, ongoing developments in modern
science and engineering are bringing increasingly grave challenges to the
conventional EC paradigm in terms of scalability. As problem scales increase,
on the one hand, the encoding spaces (i.e., dimensions of the decision vectors)
are intrinsically larger; on the other hand, EC algorithms often require
growing numbers of function evaluations (and probably larger population sizes
as well) to work properly. To meet such emerging challenges, not only does it
require delicate algorithm designs, but more importantly, a high-performance
computing framework is indispensable. Hence, we develop a distributed
GPU-accelerated algorithm library -- EvoX. First, we propose a generalized
workflow for implementing general EC algorithms. Second, we design a scalable
computing framework for running EC algorithms on distributed GPU devices.
Third, we provide user-friendly interfaces to both researchers and
practitioners for benchmark studies as well as extended real-world
applications. To comprehensively assess the performance of EvoX, we conduct a
series of experiments, including: (i) scalability test via numerical
optimization benchmarks with problem dimensions/population sizes up to
millions; (ii) acceleration test via a neuroevolution task with multiple GPU
nodes; (iii) extensibility demonstration via the application to reinforcement
learning tasks on the OpenAI Gym. The code of EvoX is available at
https://github.com/EMI-Group/EvoX
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