Paper Info
Title
An Effective Ensemble-Based Method for Creating On-the-Fly Surrogate Fitness Functions for Multi-objective Evolutionary Algorithms
Abstract
The task of designing electrical drives is a multi-objective optimization problem (MOOP) that remains very slow even when using state-of-the-art approaches like particle swarm optimization and evolutionary algorithms because the fitness function used to assess the quality of a proposed design is based on time-intensive finite element (FE) simulations. One straightforward solution is to replace the original FE-based fitness function with a much faster-to-evaluate surrogate. In our particular case each optimization scenario poses rather unique challenges (i.e., goals and constraints) and the surrogate models need to be constructed on-the-fly, automatically, during the run of the evolutionary algorithm. In the present research, using three industrial MOOPs, we investigated several approaches for creating such surrogate models and discovered that a strategy that uses ensembles of multi-layer perceptron neural networks and Pareto-trimmed training sets is able to produce very high quality surrogate models in a relatively short time interval.
Year
DOI
Venue
2013
10.1109/SYNASC.2013.38
SYNASC
Keywords
Field
DocType
multi-objective evolutionary algorithms, surrogate fitness evaluation, artificial neural networks, ensemble regression models,artificial neural networks
Interactive evolutionary computation,Mathematical optimization,Evolutionary robotics,Evolutionary algorithm,Computer science,Evolutionary computation,Surrogate model,Fitness function,Fitness approximation,Artificial intelligence,Optimization problem,Machine learning
Conference
ISSN
Citations 
PageRank 
2470-8801
1
0.37
References 
Authors
6
5
Name
Order
Citations
PageRank
Alexandru-Ciprian Zavoianu1486.37
Edwin Lughofer2194099.72
Gerd Bramerdorfer3555.92
Wolfgang Amrhein4788.29
Erich Peter Klement5989128.89