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 …

An overview of evolutionary algorithms is presented covering genetic algorithms, evolution
strategies, genetic programming and evolutionary programming. The schema theorem is …

Many seemingly different problems in machine learning, artificial intelligence, and symbolic
processing can be viewed as requiring the discovery of a computer program that produces …

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 …

Genetic programming (GP), one of the most advanced forms of evolutionary computation,
has been highly successful as a technique for getting computers to automatically solve …

(i) We investigate spectral and geometric properties of the mutation-crossover operator in a
genetic algorithm with general-size alphabet. By computing spectral estimates, we show …

The human mind possesses the most remarkable ability to perform multiple tasks with
apparent simultaneity. In fact, with the present-day explosion in the variety and volume of …

Traditional feature selection methods based on genetic algorithms randomly evolve using
unguided crossover operators and mutation operators. This leads to many inferior solutions …

This paper proposes a graph-based evolutionary algorithm called Genetic Network
Programming (GNP). Our goal is to develop GNP, which can deal with dynamic …

Evolutionary computation techniques have had limited capabilities in solving large-scale
problems due to the large search space demanding large memory and much longer training …