Abstract Evolutionary Ensemble Learning (EEL) provides a general approach for scaling
evolutionary learning algorithms to increasingly complex tasks. This is generally achieved …

Evolutionary feature construction is a key technique in evolutionary machine learning, with
the aim of constructing high-level features that enhance performance of a learning algorithm …

Multitask genetic programming methods have been applied to various domains, such as
classification, regression, and combinatorial optimization problems. Most existing multitask …

A typical machine learning development cycle maximizes performance during model
training and then minimizes the memory and area footprint of the trained model for …

Optimal sensor location methods are crucial to realize a sensor profile that achieves pre-
defined performance criteria as well as minimum cost. In recent times, indoor cultivation …

Just over a decade ago, the first comprehensive review on the state of benchmarking in
Genetic Programming (GP) analyzed the mismatch between the problems that are used to …

We consider the problem of optimizing a controller for agents whose observation and action
spaces are continuous, ie, where the controller is a multivariate real function f: R n→ R m …

Searching for symbolic models plays an important role in a wide range of domains such as
neural architecture search and automatic program synthesis. Genetic programming is a …

The current research introduces a comparison study of the utilization of artificial neural
networks (ANN) and genetic programming (GP) for predicting the optical behavior of …

Linear genetic programming (LGP) is a genetic programming paradigm based on a linear
sequence of instructions being executed. An LGP individual can be decoded into a directed …