Black-box optimization benchmarking for noiseless function testbed using an EDA and PSO hybrid

Authors:
Mohammed El-Abd;Mohamed S. Kamel
Affiliations:
University of Waterloo, Waterloo, ON, Canada;University of Waterloo, Waterloo, ON, Canada
Venue:
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Year:
2009

Citing 2
Cited 2

The correlation-triggered adaptive variance scaling IDEA

Proceedings of the 8th annual conference on Genetic and evolutionary computation
Preventing premature convergence in a PSO and EDA hybrid

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation

Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009

Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
The continuous differential ant-stigmergy algorithm for numerical optimization

Computational Optimization and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper benchmarks an Estimation of Distribution Algorithm (EDA) and Particle Swarm Optimizer (PSO) on noise-free BBOB 2009 testbed. The algorithm is referred to as EDA-PSO and further enhanced with correlation-triggered adaptive variance scaling.