New factorial design theoretic crossover operator for parametrical problem

Authors:
Kit Yan Chan;M. Emin Aydin;Terence C. Fogarty
Affiliations:
Faculty of Engineering, Science and Technology, South Bank University, London;Faculty of Engineering, Science and Technology, South Bank University, London;Faculty of Engineering, Science and Technology, South Bank University, London
Venue:
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Year:
2003

Citing 9
Cited 1

Quality Engineering Using Robust Design

Quality Engineering Using Robust Design
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Epistasis in Genetic Algorithms: An Experimental Design Perspective

Proceedings of the 6th International Conference on Genetic Algorithms
Building Better Test Functions

Proceedings of the 6th International Conference on Genetic Algorithms
A GA-Based Systematic Reasoning Approach for Solving Traveling Salesman Problems Using an Orthogonal Array Crossove

HPC '00 Proceedings of the The Fourth International Conference on High-Performance Computing in the Asia-Pacific Region-Volume 2 - Volume 2
Design and Analysis of Experiments

Design and Analysis of Experiments
An orthogonal genetic algorithm for multimedia multicast routing

IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster

IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization

IEEE Transactions on Evolutionary Computation

Hybrid sliding level Taguchi-based particle swarm optimization for flowshop scheduling problems

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recent research shows that factorial design methods improve the performance of the crossover operator in evolutionary computation. However the methods employed so far ignore the effects of interaction between genes on fitness, i.e. "epistasis". Here we propose the application of a systematic method for interaction effect analysis to enhance the performance of the crossover operator. It is shown empirically that the proposed method significantly outperforms existing crossover operators on benchmark problems with high interaction between the variables.