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Bloat control operators and diversity in genetic programming: a comparative study.

E Alfaro-Cid¹, J J Merelo, F Fernández de Vega

¹Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Valencia, Spain. evalfaro@iti.upv.es

Evolutionary Computation

|March 10, 2010

Summary

This summary is machine-generated.

The prune and plant genetic operator effectively reduces bloat in genetic programming, maintaining solution quality while significantly decreasing tree size. It outperforms other bloat control methods in certain domains.

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Area of Science:

Computer Science
Artificial Intelligence
Evolutionary Computation

Background:

Bloat, the excessive growth of individuals in genetic programming, is a significant challenge.
Existing bloat control methods have varying degrees of effectiveness and impact on solution quality.

Purpose of the Study:

To evaluate the efficacy of the prune and plant genetic operator for bloat control in genetic programming.
To compare prune and plant against established bloat control techniques.

Main Methods:

A thorough study was conducted using a set of benchmark problems.
The prune and plant operator was implemented and tested.
Performance was evaluated based on solution fitness, tree size reduction, time consumption, and diversity metrics (phenotypic and genotypic).

Main Results:

Prune and plant significantly reduced mean tree size across all tested problem domains while preserving solution fitness.
In one domain, prune and plant outperformed other methods in fitness, size reduction, and time efficiency.
The study analyzed the impact of prune and plant on phenotypic and genotypic diversity.

Conclusions:

The prune and plant operator is a viable and effective method for controlling bloat in genetic programming.
The choice of bloat control method can influence genetic pool diversity, impacting overall performance.
This research provides guidance for practitioners in selecting appropriate bloat control strategies.