Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

6-2004

Abstract

In evolutionary algorithms a critical parameter that must be tuned is that of selection pressure. If it is set too low then the rate of convergence towards the optimum is likely to be slow. Alternatively if the selection pressure is set too high the system is likely to become stuck in a local optimum due to a loss of diversity in the population. The recent Fitness Uniform Selection Scheme (FUSS) is a conceptually simple but somewhat radical approach to addressing this problem - rather than biasing the selection towards higher fitness, FUSS biases selection towards sparsely populated fitness levels. In this paper we compare the relative performance of FUSS with the well known tournament selection scheme on a range of problems.

Keywords

Selection schemes, fitness evaluation, optimization, fitness landscapes, basic working principles of evolutionary computations, (self)adaptation, evolutionary algorithm, deceptive & multimodal optimization problems.

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Publication

CEC2004: Proceedings of the Congress on Evolutionary Computation: June 19-23, Portland, OR

First Page

2144

Last Page

2151

ISBN

9780780385153

Identifier

10.1109/CEC.2004.1331162

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/CEC.2004.1331162

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