Academic Positions

  • Present 2011

    Full Professor

    Federal University of Goiás, Graduate School of Computer Sciences

  • 2016 2014

    Co-Head of computer science doctoral program

    Federal University of Goiás, Graduate School of Computer Sciences

  • Present 2015

    Editorial Board Member

    Journal of Computer Science


  • Ph.D. 2010

    Ph.D. in Electronic and Computer Engineering

    Technological Institute of Aeronautics (in portuguese, Instituto Tecnológico de Aeronáutica - ITA)

  • Sc.M.2007

    Master of Electrical Engineering

    University of São Paulo

  • B.A. (with Honors)2005

    Bachelor of Computer Engineering

    Pontifical Catholic University of Goiás

Honors, Awards and Grants

  • 2015
    Best Conference Papers
    "Node-Depth Encoding with Recombination for Multi-Objective Evolutionary Algorithm to Solve Loss Reduction Problem in Large-scale Distribution Systems", IEEE Power & Energy Society General Meeting.
  • 2014
    The Best-Five Papers Of Electronics Letters 50th Edition, Highlighted In Its Cover
    (Refer to paper "Mutation-based compact genetic algorithm for spectroscopy variable selection"), The Institution of Engineering and Technology - IET.
  • 2014
    Theses and Dissertations Competition
    Second Place in Theses and Dissertations Competition in Computer Architecture and High Performance Computing (Advisor of Lauro Cássio Martins de Paula), Brazilian Computer Society.
  • 2012
    Honorable Mention
    Guest paper for publication in International Journal of Natural Computing Research (Referring to the Best Work of the 3rd Evolutionary Computation Luso-Brazilian School), University of Minho, Portugal.
  • 2008
    Panel Awarded
    Annual Meeting of the Brazilian Society of Chemistry, Brazilian Chemical Society.
  • 2004
    Scientific Initiation
    Tower of Hanoi (Scientific Initiation), Department of Computer Science - Pontifical Catholic University of Goiás.

Combining exhaustive search and multi-objective evolutionary algorithm for service restorat ion in large -scale distribution systems.

Camillo, M. H. M; Fanucchi, R. Z. ; Romero, M. E. V. ; Lima, T. W. ; Soares, A. S. ; Delbem, A. C. B. ; Marques, L. T. ; Maciel, C. D. ; London Junior, J. B. A.
Journal Paper Electric Power Systems Research (Print), v. 134, p. 1-8, 2016.


Service restoration problem in distribution systems emerges after the faulted areas have been identified and isolated. A solution is obtained by determining the minimal number of switching operations that results in a configuration with a minimal number of healthy out-of-service areas without violating the operational and radiality constraints. Recently a practical and efficient methodology was developed and demonstrated through tests performed on the real and large-scale distribution system of Londrina city (Brazil). This methodology, named MEAN-MH, combines a Multi-objective Evolutionary Algorithm with Node-Depth Encoding, Multiple criteria tables and an alarming Heuristic. As any methodology based on meta-heuristics, the MEAN-MH does not guarantee to find the optimal solution of the service restoration problem, even when the optimal solution requires operations only in normally open switches incident to the healthy out-of-service areas (named as Tier 1 normally open switches). This paper proposes an extension of MEAN-MH that incorporates an Exhaustive Search (ES) procedure as a previous stage before MEAN-MH. The proposed ES guarantees the generation and analysis of all possible radial configurations that restore the service to all healthy out-of-service areas requiring operations only in Tier 1 normally open switches. Therefore, when the optimal solution of the service restoration problem requires operations only in Tier 1 NO switches, the proposed methodology, named MEAN-MH+ES, guarantees the optimum. However, when the optimal solution requires operations also in other switches, the MEAN-MH+ES searches by a feasible solution minimizing both the number of switching operations and the number of healthy out-of-service areas. To demonstrate the effectiveness of the proposal, both MEAN-MH and MEAN-MH+ES are applied to two real and large-scale distribution systems of Brazil. Moreover, the results obtained by MEAN-MH+ES are compared with those found in another published work.

Node-depth phylogenetic-based encoding, a spanning-tree representation for evolutionary algorithms. part I: Proposal and properties analysis.

Lima, Telma Woerle De; Delbem, Alexandre Cláudio Botazzo ; Soares, Anderson Da Silva ; Federson, Fernando Marques ; Junior, João Bosco Augusto London ; Baalen, Jeffrey Van
Journal Paper Swarm and Evolutionary Computation, 2016.


Representation choice and the development of search operators are crucial aspects of the efficiency of Evolutionary Algorithms (EAs) in combinatorial problems. Several researchers have proposed representations and operators for EAs that manipulate spanning trees. This paper proposes a new encoding called Node-depth Phylogenetic-based Encoding (NPE). NPE represents spanning trees by the relation between nodes and their depths using a relatively simple codification/decodification process. The proposed NPE operators are based on methods used for tree rearrangement in phylogenetic tree reconstruction: subtree prune and regraft; and tree bisection and reconstruction. NPE and its operators are designed to have high locality, feasibility, low time complexity, be unbiased, and have independent weight. Therefore, NPE is a good choice of data structure for EAs applied to network design problems.

A feasibility cachaca type recognition using computer vision and pattern recognition.

Rodrigues, B. U.; Soares, A. S. ; Costa, R. M. ; Baalen, J. V. ; Salvini, R. L. ; Delbem, A. C. B. ; Coelho, C. J. ; Laureano, G. T.
Journal Paper Computers and Electronics in Agriculture, v. 123, p. 410 -414, 2016.


Brazilian rum (also known as cachaça) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters produced each year. It is a traditional drink with refined features and a delicate aroma that is produced mainly in Brazil but consumed in many countries. It can be aged in various types of wood for 1–3 years, which adds aroma and a distinctive flavor with different characteristics that affect the price. A research challenge is to develop a cheap automatic recognition system that inspects the finished product for the wood type and the aging time of its production. Some classical methods use chemical analysis, but this approach requires relatively expensive laboratory equipment. By contrast, the system proposed in this paper captures image signals from samples and uses an intelligent classification technique to recognize the wood type and the aging time. The classification system uses an ensemble of classifiers obtained from different wavelet decompositions. Each classifier is obtained with different wavelet transform settings. We compared the proposed approach with classical methods based on chemical features. We analyzed 105 samples that had been aged for 3 years and we showed that the proposed solution could automatically recognize wood types and the aging time with an accuracy up to 100.00% and 85.71% respectively, and our method is also cheaper.

Mutation-based compact genetic algorithm for spectroscopy variable selection in determining protein concentration in wheat grain.

Soares, A. S.; Delbem, A. C. B. ; Lima, T. W. ; Coelho, C. J. ; Baalen, J. V. ; Federson, F. M. ; Soares, F. A. A. M. N.
Journal Paper Ele ctronics Letters, v. 50, p. 932 -934, 2014.


Wheat is the third most produced grain in the world after maize and rice. Determining the protein concentration in wheat grain is one of the major challenges for measuring its industrial quality. Samples of wheat can be collected using a spectrophotometer device. The challenge is to associate the energy absorbed by the device with the protein concentration in wheat. The device measures hundreds of variable intensities that can be related to the physicochemical properties. The selection of a subset of uncorrelated variables has been shown to be fundamental for establishing correct correlations and reducing prediction error. A new formulation of a compact genetic algorithm that uses only a mutation operator is proposed. The results produced by the proposed approach are compared with traditional techniques for spectroscopy variable selection as successive projection algorithms, partial least square and classical formulations of genetic algorithms. For near-infrared spectral analysis of the protein concentration in wheat, the prediction errors decreased from 0.28 to 0.10 on average, a reduction of 63%.

A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems.

Paula, L. C. M.; Soares, A. S. ; Delbem, A. C. B. ; Coelho, C. J. ; Lima, T. W. ; Galvao Filho, A. R.
Journal Paper Plos One, v. 9, p. e114145, 2014.


Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.

About 70 more. For full list of publications click here (in portuguese)