Time-dependent performance comparison of evolutionary algorithms

Authors:
David Shilane;Jarno Martikainen;Seppo J. Ovaska
Affiliations:
University of California, Berkeley;Helsinki University of Technology;Helsinki University of Technology
Venue:
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Year:
2009

Citing 7
Cited 0

Evolutionary Computation: Towards a New Philosophy of Machine Intelligence

Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
A general framework for statistical performance comparison of evolutionary computation algorithms

Information Sciences: an International Journal
Statistical analysis of the main parameters involved in the designof a genetic algorithm

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization

IEEE Transactions on Evolutionary Computation
Design of evolutionary algorithms-A statistical perspective

IEEE Transactions on Evolutionary Computation
Statistical exploratory analysis of genetic algorithms

IEEE Transactions on Evolutionary Computation
Statistical analysis of the parameters of a neuro-genetic algorithm

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a statistical methodology for comparing the performance of evolutionary algorithms that iteratively generate candidate optima over the course of many generations. Performance data are analyzed using multiple hypothesis testing to compare competing algorithms. Such comparisons may be drawn for general performance metrics of any iterative evolutionary algorithm with any data distribution. We also propose a data reduction technique to reduce computational costs.