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

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Published on: December 9, 2012

On the effect of populations in evolutionary multi-objective optimisation.

Oliver Giel¹, Per Kristian Lehre

¹Fakultät für Informatik, Technische Universität Dortmund, Germany. Oliver.Giel@cs.uni-dortmund.de

Evolutionary Computation

|June 22, 2010

Summary

Population-based multi-objective evolutionary algorithms (MOEAs) are crucial for certain problems. Our study shows single-individual algorithms fail where population-based methods like SEMO succeed due to an exponential runtime gap.

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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Area of Science:

Computer Science
Artificial Intelligence
Optimization

Background:

Multi-objective evolutionary algorithms (MOEAs) are widely used for complex optimization.
The role of population size in MOEA performance remains an open research question.

Purpose of the Study:

To investigate the necessity of populations in MOEAs.
To identify specific problem characteristics that necessitate population-based approaches.

Main Methods:

Introduced two novel bi-objective problems designed to highlight population requirements.
Conducted rigorous runtime analysis comparing population-based and single-individual algorithms.

Main Results:

Demonstrated an exponential runtime gap between the population-based Simple Evolutionary Multi-Objective Optimiser (SEMO) and single-individual algorithms.
Showcased that SEMO successfully solves the proposed problems, while single-individual algorithms fail.

Conclusions:

Population-based MOEAs are essential for specific multi-objective problems.
The findings underscore the importance of population dynamics in achieving successful optimization outcomes.