Related Experiment Video
Updated: Aug 16, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
A filter-based evolutionary algorithm for constrained optimization
Lauren Clevenger1, Lauren Ferguson, William E Hart
1Sandia National Laboratories, Discrete Algorithms and Mathematics Dept, P.O. Box 5800, MS 1110, Albuquerque, New Mexico 87185-1110, USA. Lmcleve@aol.com
Abstract:
We introduce a filter-based evolutionary algorithm (FEA) for constrained optimization. The filter used by an FEA explicitly imposes the concept of dominance on a partially ordered solution set. We show that the algorithm is provably robust for both linear and nonlinear problems and constraints. FEAs use a finite pattern of mutation offsets, and our analysis is closely related to recent convergence results for pattern search methods. We discuss how properties of this pattern impact the ability of an FEA to converge to a constrained local optimum.
Related Concept Videos
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Optimization Problems
Lagrange Multipliers: Two Constraints
Lagrange Multipliers: One Constraint
Application of Nonlinear Inequalities
Differential Equations: Problem Solving
