Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks (GANs)

10/11/2019
by   Cheng He, et al.
20

Recently, more and more 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.

READ FULL TEXT

page 3

page 4

page 5

page 6

page 8

page 12

page 13

page 14

07/10/2019

Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks

Recently, more and more works have proposed to drive evolutionary algori...
07/13/2020

Exploring the Evolution of GANs through Quality Diversity

Generative adversarial networks (GANs) achieved relevant advances in the...
08/03/2017

Preselection via Classification: A Case Study on Evolutionary Multiobjective Optimization

In evolutionary algorithms, a preselection operator aims to select the p...
02/20/2020

Uncovering Coresets for Classification With Multi-Objective Evolutionary Algorithms

A coreset is a subset of the training set, using which a machine learnin...
02/20/2021

Augmenting High-dimensional Nonlinear Optimization with Conditional GANs

Many mathematical optimization algorithms fail to sufficiently explore t...
01/08/2021

Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks

Large-scale multiobjective optimization problems (LSMOPs) are characteri...
04/09/2020

Using Skill Rating as Fitness on the Evolution of GANs

Generative Adversarial Networks (GANs) are an adversarial model that ach...