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Related Experiment Video

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Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

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Fuzzy Pattern Tree Evolution Using Grammatical Evolution.

Aidan Murphy¹, Muhammad Sarmad Ali¹, Douglas Mota Dias^1,2

¹Lero and University of Limerick, Limerick, Ireland.

SN Computer Science

|August 11, 2022

Summary

This summary is machine-generated.

A new method, Fuzzy Grammatical Evolution, creates effective Fuzzy Pattern Trees for classification tasks. It outperforms existing methods like Cartesian Genetic Programming, offering better performance and fewer parameters.

Keywords:

Fuzzy logic Grammatical evolution Pattern trees

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Fuzzy Pattern Trees (FPTs) are used for classification.
Existing methods for evolving FPTs include Cartesian Genetic Programming (CGP).

Purpose of the Study:

To introduce a novel approach for evolving Fuzzy Pattern Trees using Grammatical Evolution, termed Fuzzy Grammatical Evolution (FGE).
To compare the performance of FGE against state-of-the-art methods, specifically CGP-evolved FPTs.

Main Methods:

Fuzzy Grammatical Evolution (FGE) was developed to induce Fuzzy Pattern Trees.
FGE was applied to benchmark classification problems.
Ensemble methods, including Boosting, were investigated to enhance FGE performance.
A version of FGE incorporating parsimony pressure was tested to address tree bloat.

Main Results:

FGE achieved comparable or superior results to CGP on benchmark classification problems.
FGE produced better-performing trees than CGP, despite CGP generating smaller trees.
FGE requires fewer user-selectable parameters than CGP.
FGE with parsimony pressure generated smaller trees than CGP without performance compromise.
Boosting improved FGE performance on half of the investigated benchmarks.

Conclusions:

Fuzzy Grammatical Evolution is a promising alternative for evolving high-performing Fuzzy Pattern Trees.
FGE offers advantages over CGP in terms of performance and parameter tuning.
Parsimony pressure can effectively mitigate bloat in FGE without sacrificing accuracy.
Ensemble techniques, particularly Boosting, can further enhance FGE's effectiveness.