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Modeling the performance of evolutionary algorithms on the root identification problem: a case study with PBIL and

Enrique Yeguas¹, Robert Joan-Arinyo, Mar A Victoria Luz N

¹Departamento de Informática y Análisis Numérico, Universidad de Córdoba, Córdoba, 14071, Spain. eyeguas@uco.es

Evolutionary Computation

|September 3, 2010

Summary

A new statistical model measures evolutionary algorithm performance for complex problems. This model predicts solution quality based on runtime or estimates iterations needed for a desired solution quality, aiding computational research.

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Area of Science:

Computer Science
Computational Mathematics

Background:

Measuring evolutionary algorithm performance is crucial for computationally intensive problems.
A performance model can predict solution quality versus runtime or estimate iterations for a target quality.

Purpose of the Study:

To develop a statistical model for evaluating PBIL and CHC evolutionary algorithms.
To apply this model to the root identification problem in constraint-based modeling.

Main Methods:

Developed a statistical performance model.
Empirically validated the model using a benchmark with large search spaces.
Focused on Population-Based Incremental Learning (PBIL) and CHC algorithms.

Main Results:

The statistical model accurately describes the performance of PBIL and CHC algorithms.
The model's predictions were validated on extensive benchmarks.
Demonstrated the model's utility for the root identification problem.

Conclusions:

The developed statistical model provides a reliable method for assessing evolutionary algorithm performance.
This model is valuable for problems requiring high computational resources, such as root identification.
Empirical validation confirms the model's effectiveness across large search spaces.