Over recent years, there has been a rapid development of deep learning (DL) in both
industry and academia fields. However, finding the optimal hyperparameters of a DL model …

Classification is one of the most researched questions in machine learning and data mining.
A wide range of real problems have been stated as classification problems, for example …

Background: Mobile phone sensors can be used to develop context-aware systems that
automatically detect when patients require assistance. Mobile phones can also provide …

One of the major challenges in pattern recognition problems is the feature extraction process
which derives new features from existing features, or directly from raw data in order to …

Objective Medical data sets are usually small and have very high dimensionality. Too many
attributes will make the analysis less efficient and will not necessarily increase accuracy …

Evolutionary Computation (EC) holds great promise for computer science today. After an
early start in the 1950s, it was pursued by a handful of scientists until it took off as a rapidly …

This authoritative handbook reveals the connections between bioinspired techniques and
the development of solutions to problems that arise in diverse problem domains. It provides …

Parsimony pressure, the explicit penalization of larger programs, has been increasingly
used as a means of controlling code growth in genetic programming. However, in many …

This paper proposes a novel method for breast cancer diagnosis using the feature
generated by genetic programming (GP). We developed a new feature extraction measure …

This paper describes a domain-independent approach to the use of genetic programming
for object detection problems in which the locations of small objects of multiple classes in …