Evolutionary Computation (EC) approaches are inspired by nature and solve optimization
problems in a stochastic manner. They can offer a reliable and effective approach to address …

Behavior Trees (BTs) provide a way to structure the behavior of an artificial agent such as a
robot or a non-player character in a computer game. Traditional design methods, such as …

Classification and regression trees, as well as their variants, are off-the-shelf methods in
Machine Learning. In this paper, we review recent contributions within the Continuous …

This paper presents a survey of evolutionary algorithms that are designed for decision-tree
induction. In this context, most of the paper focuses on approaches that evolve decision …

The induction of decision trees is a widely-used approach to build classification models that
guarantee high performance and expressiveness. Since a recursive-partitioning strategy …

With the rapid growth of databases in many modern enterprises data mining has become an
increasingly important approach for data analysis. The operations research community has …

In this paper, we study the problem of automatically synthesizing a successful behavior tree
(BT) in an a priori unknown dynamic environment. Starting with a given set of actions, a …

A decision tree (DTs) is one of the most popular machine learning algorithms that divide
data repeatedly to form groups or classes. It is a supervised learning algorithm that can be …

Previous work on the solution to analytical electronic customer relationship management
(eCRM) problems has used either data-mining (DM) or optimization methods, but has not …

In this paper, we present a new algorithm for learning oblique decision trees. Most of the
current decision tree algorithms rely on impurity measures to assess the goodness of …