Explainable artificial intelligence (XAI) has received great interest in the recent decade, due
to its importance in critical application domains, such as self-driving cars, law, and …

The latest book on Genetic Programming, Poli, Langdon and McPhee's (with contributions
from John R. Koza) A Field Guide to Genetic Programming represents an exciting landmark …

Bloat can be defined as an excess of code growth without a corresponding improvement in
fitness. This problem has been one of the most intensively studied subjects since the …

This paper reports an improvement to genetic programming (GP) search for the symbolic
regression domain, based on an analysis of dissimilarity and mating. GP search is generally …

Genetic programming is a metaheuristic search method that uses a population of variable-
length computer programs and a search strategy based on biological evolution. The idea of …

Classification is a supervised machine learning process that categories an instance based
on a number of features. The process of classification involves several stages, including …

The fitness landscape of a problem is the relation between the solution candidates and their
reproduction probability. In order to understand optimization problems, it is essential to also …

Evolutionary feature construction is an important technique in the machine learning domain
for enhancing learning performance. However, traditional genetic programming-based …

Recombination is a commonly used genetic operator in artificial and computational
evolutionary systems. It has been empirically shown to be essential for evolutionary …

Motivated by biological inspiration and the issue of instruction disruption, we develop a new
form of Linear Genetic Programming (LGP) called Parallel LGP (PLGP) for classification …