Biography

Lucas Marcondes Pavelski is a PhD student at Universidade Tecnológica Federal do Paraná. His research interests include optimization, machine learning and data analysis. He works as IT Analyst at Universidade Federal do Paraná focusing on web development.

Interests

  • Optimization
  • Machine Learning
  • Data Analysis

Education

  • PhD in Electrical and Computer Engineering, 2017

    Universidade Tecnológica Federal do Paraná

  • MEng in Electrical and Computer Engineering, 2013

    Universidade Tecnológica Federal do Paraná

  • BSc in Computer Science, 2009

    Universidade Estadual do Centro-Oeste de Paraná

Experience

 
 
 
 
 

IT Analist

Universidade Federal do Paraná

Aug 2017 – Present Curitiba, Brazil

Responsibilities include:

  • Web development: Java EE and Vue.js
  • Database management and reports: MariaDb and Shiny
  • Support and deploying: Payara and Docker
 
 
 
 
 

Professor

Universidade Estadual do Centro-Oeste do Paraná

Jan 2015 – Jan 2017 Guarapuava, Brazil
Taught classes on: computer programming, computer organization, data structures, computer networks, databases, graph algorithms and computer theory.

Recent Publications

An empirical analysis of constraint handling on evolutionary multi-objective algorithms for the Environmental/Economic Load Dispatch problem

This paper analyses different multi-objective evolutionary algorithms to deal with the Environmental/Economic Load Dispatch (EELD). …
Josiel Neumann Kuk, Richard Aderbal Gonçalves, Lucas Marcondes Pavelski, Sandra Mara Guse Scós Venske, Carolina Paula de Almeida, Aurora Trinidad Ramirez Pozo
PDF DOI

Meta-Learning on Flowshop Using Fitness Landscape Analysis

In the context of recommendation methods, meta-learning considers the use of previous knowledge regarding problems solution and …
Lucas Marcondes Pavelski, Myriam Regattieri Delgado, Marie-Éléonore Kessaci
PDF DOI

Recommending Meta-Heuristics and Configurations for the Flowshop Problem via Meta-Learning: Analysis and Design

This work proposes a meta-learning system based on Gradient Boosting Machines to recommend local search heuristics for solving flowshop …
Lucas Marcondes Pavelski, Marie-Éléonore Kessaci, Myriam Regattieri Delgado
DOI

Meta-Learning for Optimization: A Case Study on the Flowshop Problem Using Decision Trees

This work investigates the use of meta-learning for optimization tasks when a classic operational research problem (flowshop) is …
Lucas Marcondes Pavelski, Myriam Regattieri Delgado, Marie-Éléonore Kessaci
DOI

Adaptive Operator Selection for Many-Objective Optimization with NSGA-III

The number of objectives in real-world problems has increased in recent years and better algorithms are needed to deal efficiently with …
Richard A. Gonçalves, Lucas M. Pavelski, Carolina P. de Almeida, Josiel N. Kuk, Sandra M. Venske, Myriam R. Delgado
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