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 introduce a novel combinatorial optimization problem: the one-commodity traveling salesman problem with selective pickup and delivery (1-TSP-SELPD), characterized by the fact th...
Abstract— We present a unifying framework for continuous optimization and sampling. This framework is based on Gaussian Adaptation (GaA), a search heuristic developed in the late...
— The efficient diagnosis of hardware and software faults in parallel and distributed systems remains a challenge in today’s most prolific decentralized environments. System-...
— This work investigates the effects of the periodization of local and global multi-objective search algorithms. To this, we introduce a model for periodization and define a new...
— Due to developing mobile devices and providing services like mobile blogs, people can easily share their thought and experience, at any place and any time. A picture is an impo...
—Common explanations of DE’s search behaviour as its crossover rate Cr is varied focus on the directionality of the search, as low values make moves aligned with a small number...
— Ant-colony optimization (ACO) is a popular swarm intelligence metaheuristic scheme that can be applied to almost any optimization problem. In this paper, we address a performan...
—In this paper we apply Coevolutionary Temporal Difference Learning (CTDL), a hybrid of coevolutionary search and reinforcement learning proposed in our former study, to evolve s...
— This work is the first attempt to investigate the neural dynamics of a simulated robotic agent engaged in minimally cognitive tasks by employing evolved instances of the Kuram...