V
Vinícius Veloso de Melo
Researcher at Federal University of São Paulo
Publications - 61
Citations - 807
Vinícius Veloso de Melo is an academic researcher from Federal University of São Paulo. The author has contributed to research in topics: Metaheuristic & Genetic programming. The author has an hindex of 12, co-authored 60 publications receiving 602 citations. Previous affiliations of Vinícius Veloso de Melo include Memorial University of Newfoundland & University of São Paulo.
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Journal ArticleDOI
Defining and simulating open-ended novelty: requirements, guidelines, and challenges.
Wolfgang Banzhaf,Bert Baumgaertner,Guillaume Beslon,René Doursat,James A. Foster,Barry McMullin,Vinícius Veloso de Melo,Thomas Miconi,Lee Spector,Susan Stepney,Roger White +10 more
TL;DR: This work defines an architecture suitable for building simulations of open-ended novelty-generating systems and discusses the design principles applicable to those systems and closes with some challenges for the community.
Journal ArticleDOI
Investigating Multi-View Differential Evolution for solving constrained engineering design problems
TL;DR: A Multi-View Differential Evolution algorithm (MVDE) in which several mutation strategies are applied to the current population to generate different views at each iteration, resulting in automatic exploration/exploitation balance is proposed.
Journal ArticleDOI
An improved Jaya optimization algorithm with Lévy flight
TL;DR: Numerical results show that, although both Jaya and LJA are in general less efficient than the most advanced algorithms on the CEC 2014 benchmark, LJA largely outperforms the original Jaya algorithm in most cases, and is also highly competitive on the tested industrial problems.
Proceedings ArticleDOI
Kaizen programming
TL;DR: Experiments on benchmark functions proposed in the literature show that Kaizen Programming easily outperforms Genetic Programming and other methods, providing high quality solutions for both training and testing sets while requiring a small number of function evaluations.
Journal ArticleDOI
A modified Covariance Matrix Adaptation Evolution Strategy with adaptive penalty function and restart for constrained optimization
TL;DR: A modified Covariance Matrix Adaptation Evolution Strategy specifically designed for solving constrained optimization problems, which presents competitive results on a broad set of benchmark functions and engineering problems, outperforming most state-of-the-art algorithms as for both efficiency and constraint handling.