Enhancing hierarchical surrogate-assisted evolutionary algorithm for high-dimensional expensive optimization via random projection

by   Xiaodong Ren, et al.

By remarkably reducing real fitness evaluations, surrogate-assisted evolutionary algorithms (SAEAs), especially hierarchical SAEAs, have been shown to be effective in solving computationally expensive optimization problems. The success of hierarchical SAEAs mainly profits from the potential benefit of their global surrogate models known as "blessing of uncertainty" and the high accuracy of local models. However, their performance leaves room for improvement on highdimensional problems since now it is still challenging to build accurate enough local models due to the huge solution space. Directing against this issue, this study proposes a new hierarchical SAEA by training local surrogate models with the help of the random projection technique. Instead of executing training in the original high-dimensional solution space, the new algorithm first randomly projects training samples onto a set of low-dimensional subspaces, then trains a surrogate model in each subspace, and finally achieves evaluations of candidate solutions by averaging the resulting models. Experimental results on six benchmark functions of 100 and 200 dimensions demonstrate that random projection can significantly improve the accuracy of local surrogate models and the new proposed hierarchical SAEA possesses an obvious edge over state-of-the-art SAEAs



There are no comments yet.


page 1

page 2

page 3

page 4


PAMELI: A Meta-Algorithm for Computationally Expensive Multi-Objective Optimization Problems

We present an algorithm for multi-objective optimization of computationa...

Data-efficient Neuroevolution with Kernel-Based Surrogate Models

Surrogate-assistance approaches have long been used in computationally e...

Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms

Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisa...

Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems

Very expensive problems are very common in practical system that one fit...

A Surrogate-Assisted Variable Grouping Algorithm for General Large Scale Global Optimization Problems

Problem decomposition plays a vital role when applying cooperative coevo...

A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble Learning

We propose a novel surrogate-assisted Evolutionary Algorithm for solving...

A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning

The integration of Reinforcement Learning (RL) and Evolutionary Algorith...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.