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Explainable artificial intelligence (XAI) is an important and rapidlyexpanding research topic. The goal of XAI is to gain trust in a machinelearning (ML) model through clear insights into how the model arrives at itspredictions. Genetic programming (GP) is often cited as being uniquelywell-suited to contribute to XAI because of its capacity to learn (small)symbolic models that have the potential to be interpreted. Nevertheless, likemany ML algorithms, GP typically results in a single best model. However, inpractice, the best model in terms of training error may well not be the mostsuitable one as judged by a domain expert for various reasons, includingoverfitting, multiple different models existing that have similar accuracy, andunwanted errors on particular data points due to typical accuracy measures likemean squared error. Hence, to increase chances that domain experts deem aresulting model plausible, it becomes important to be able to explicitly searchfor multiple, diverse, high-quality models that trade-off different meanings ofaccuracy. In this paper, we achieve exactly this with a novel multi-modalmulti-tree multi-objective GP approach that extends a modern model-based GPalgorithm known as GP-GOMEA that is already effective at searching for smallexpressions.

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