Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models

TL;DR

This paper introduces a novel multi-modal multi-objective genetic programming method that efficiently finds multiple diverse, high-quality models to enhance explainability and trust in AI systems, addressing limitations of single-model approaches.

Contribution

The paper proposes a new multi-modal multi-objective GP approach based on GP-GOMEA to explicitly search for multiple diverse models, improving model interpretability and relevance.

Findings

Successfully finds multiple diverse high-quality models

Enhances explainability by providing varied models for domain experts

Extends GP-GOMEA with multi-modal multi-objective capabilities

Abstract

Explainable artificial intelligence (XAI) is an important and rapidly expanding research topic. The goal of XAI is to gain trust in a machine learning (ML) model through clear insights into how the model arrives at its predictions. Genetic programming (GP) is often cited as being uniquely well-suited to contribute to XAI because of its capacity to learn (small) symbolic models that have the potential to be interpreted. Nevertheless, like many ML algorithms, GP typically results in a single best model. However, in practice, the best model in terms of training error may well not be the most suitable one as judged by a domain expert for various reasons, including overfitting, multiple different models existing that have similar accuracy, and unwanted errors on particular data points due to typical accuracy measures like mean squared error. Hence, to increase chances that domain experts deem a resulting model plausible, it becomes important to be able to explicitly search for multiple, diverse, high-quality models that trade-off different meanings of accuracy. In this paper, we achieve exactly this with a novel multi-modal multi-tree multi-objective GP approach that extends a modern model-based GP algorithm known as GP-GOMEA that is already effective at searching for small expressions.