Many promising approaches to symbolic regression have been presented in recent years,
yet progress in the field continues to suffer from a lack of uniform, robust, and transparent …

This article pursues the insight that language models naturally enable an intelligent variation
operator similar in spirit to evolutionary crossover. In particular, language models of …

Background The selection, development, or comparison of machine learning methods in
data mining can be a difficult task based on the target problem and goals of a particular …

In this paper we provide a broad benchmarking of recent genetic programming approaches
to symbolic regression in the context of state of the art machine learning approaches. We …

The evaluation of artificial intelligence systems and components is crucial for the progress of
the discipline. In this paper we describe and critically assess the different ways AI systems …

Genetic programming (GP) is a powerful evolutionary algorithm that has been widely used
for solving many real-world optimization problems. However, traditional GP can only solve a …

We describe a broad class of problems, called “uncompromising problems,” which are
characterized by the requirement that solutions must perform optimally on each of many test …

We present the results of a community survey regarding genetic programming benchmark
practices. Analysis shows broad consensus that improvement is needed in problem …

Recent interest in the development and use of non-trivial benchmark problems for genetic
programming research has highlighted the scarcity of general program synthesis (also …

Grammatical Evolution (GE) is a population-based evolutionary algorithm, where a formal
grammar is used in the genotype to phenotype map** process. PonyGE2 is an open …