Gradient estimation in global optimization algorithms

Authors:
Megan Hazen;Maya R. Gupta
Affiliations:
Applied Physics Laboratory, University of Washington, Seattle, WA;Dept. of Electrical Engineering, University of Washington, Seattle, WA
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 8
Cited 2

Optimization theory with applications

Optimization theory with applications
A Combined Topographical Search Strategy with Ellipsometric Application

Journal of Global Optimization
Analysis of Generalized Pattern Searches

SIAM Journal on Optimization
Introduction to Stochastic Search and Optimization

Introduction to Stochastic Search and Optimization
A Hybrid Descent Method for Global Optimization

Journal of Global Optimization
Search strategies for global optimization

Search strategies for global optimization
The particle swarm - explosion, stability, and convergence in amultidimensional complex space

IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better

IEEE Transactions on Evolutionary Computation

Theory and Use of the EM Algorithm

Foundations and Trends in Signal Processing
Detection of locally relevant variables using SOM-NG algorithm

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

The role of gradient estimation in global optimization is investigated. The concept of a regional gradient is introduced as a tool for analyzing and comparing different types of gradient estimates. The correlation of different estimated gradients to the direction of the global optima is evaluated for standard test functions. Experiments quantify the impact of different gradient estimation techniques in two population-based global optimization algorithms: fully-informed particle swarm (FIPS) and multiresolutional estimated gradient architecture (MEGA).