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Updated: Mar 31, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Analyzing Evolutionary Optimization in Noisy Environments.

Chao Qian¹, Yang Yu², Zhi-Hua Zhou³

¹National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China qianc@lamda.nju.edu.cn.

Evolutionary Computation

|October 21, 2015

Summary

This summary is machine-generated.

Evolutionary algorithms (EAs) can be faster with noise on some problems, but noise harms others. A new smooth threshold selection strategy improves EA noise tolerance, as shown in experiments.

Keywords:

Noisy optimization computational complexity.evolutionary algorithms reevaluation running time threshold selection

Related Experiment Videos

Last Updated: Mar 31, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

Computational intelligence
Optimization algorithms
Stochastic metaheuristics

Background:

Optimization tasks often occur in noisy environments, yielding inexact solution evaluations.
Evolutionary algorithms (EAs) are widely used for noisy optimization, but theoretical understanding is limited.

Purpose of the Study:

Investigate the impact of noisy fitness on EA running times.
Analyze noise-handling mechanisms in EAs for noise-harmful problems.
Propose and validate a novel strategy for enhanced noise tolerance.

Main Methods:

Theoretical analysis of EA performance under noisy fitness evaluations.
Examination of reevaluation and threshold selection mechanisms.
Development and experimental validation of smooth threshold selection.
Empirical testing on synthetic and combinatorial problems (minimum spanning tree, maximum matching).

Main Results:

Identified noise-helpful problems where noise accelerates EAs.
Demonstrated that simultaneous reevaluation and threshold selection are ineffective for asymmetric one-bit noise.
Proved smooth threshold selection enhances noise tolerance.
Experimental results align with theoretical findings, confirming smooth threshold selection's efficacy.

Conclusions:

Noise can be beneficial for EAs on specific problems.
Standard noise-handling mechanisms have limitations.
Smooth threshold selection offers improved robustness for EAs in noisy environments.