The great majority of genetic programming (GP) algorithms that deal with the classification problem follow a supervised approach, i.e., they consider that all fitness cases availab...
Junio de Freitas, Gisele L. Pappa, Altigran Soares...
We propose a novel approach to encapsulate non-deterministic computations in functional logic programs. Our approach is based on set functions that return the set of all the resul...
Evaluating GP schema in context is considered to be a complex, and, at times impossible, task. The tightly linked nodes of a GP tree is the main reason behind its complexity. This...
A key problem in reinforcement learning is finding a good balance between the need to explore the environment and the need to gain rewards by exploiting existing knowledge. Much ...
We present a two-phase algorithm for solving large-scale quadratic programs (QPs). In the first phase, gradient-projection iterations approximately minimize an augmented Lagrangian...