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Felipe Campelo
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- affiliation: Aston University, UK
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2020 – today
- 2023
- [j14]Felipe Campelo
, Claus Aranha:
Lessons from the Evolutionary Computation Bestiary. Artif. Life 29(4): 421-432 (2023) - [j13]Jorge Augusto Hongo, Giovanni Marques de Castro
CALANGO: A phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species. Patterns 4(6): 100728 (2023) - [j12]Giovanni Marques de Castro, Felipe Campelo
Why are some plants taller? Researchers on the unveiling of genetic variation associated with complex quantitative phenotypes. Patterns 4(6): 100774 (2023) - [c24]Jodie Ashford, Anikó Ekárt, Felipe Campelo
Estimating the Limits of Organism-Specific Training for Epitope Prediction. BIBM 2023: 4195-4202 - 2022
- [j11]Reem Abukmeil, Ahmad Al-Mallahi
New approach to estimate macro and micronutrients in potato plants based on foliar spectral reflectance. Comput. Electron. Agric. 198: 107074 (2022) - [j10]Claus Aranha
Metaphor-based metaheuristics, a call for action: the elephant in the room. Swarm Intell. 16(1): 1-6 (2022) - 2021
- [j9]Jodie Ashford, João Reis-Cunha, Igor Lobo, Francisco Pereira Lobo, Felipe Campelo
Organism-specific training improves performance of linear B-cell epitope prediction. Bioinform. 37(24): 4826-4834 (2021) - [c23]Luís Paulo F. Garcia
Evaluating Clustering Meta-features for Classifier Recommendation. BRACIS (1) 2021: 453-467 - [c22]Eduardo G. Carrano
Diversity-Driven Selection Operator for Combinatorial Optimization. EMO 2021: 178-190 - [c21]Felipe Campelo, Claus Aranha:
Sharks, Zombies and Volleyball: Lessons from the Evolutionary Computation Bestiary. LIFELIKE 2021 - 2020
- [j8]Felipe Campelo
Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances. J. Heuristics 26(6): 851-883 (2020) - [j7]André L. Maravilha
A recombination-based matheuristic for mixed integer programming problems with binary variables. Int. Trans. Oper. Res. 27(1): 418-434 (2020) - [c20]Yuri Cossich Lavinas, Claus Aranha, Marcelo Ladeira, Felipe Campelo
MOEA/D with Random Partial Update Strategy. CEC 2020: 1-8 - [i6]Yuri Cossich Lavinas, Claus Aranha, Marcelo Ladeira, Felipe Campelo:
MOEA/D with Random Partial Update Strategy. CoRR abs/2001.06980 (2020) - [i5]Lucas S. Batista, Felipe Campelo
The Cone epsilon-Dominance: An Approach for Evolutionary Multiobjective Optimization. CoRR abs/2008.04224 (2020)
2010 – 2019
- 2019
- [j6]Athila R. Trindade
Tuning metaheuristics by sequential optimisation of regression models. Appl. Soft Comput. 85 (2019) - [j5]Felipe Campelo
Sample size estimation for power and accuracy in the experimental comparison of algorithms. J. Heuristics 25(2): 305-338 (2019) - [c19]Jodie Ashford, Jordan J. Bird, Felipe Campelo
Classification of EEG Signals Based on Image Representation of Statistical Features. UKCI 2019: 449-460 - [i4]Felipe Campelo, Elizabeth F. Wanner:
Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances. CoRR abs/1908.01720 (2019) - 2018
- [i3]Felipe Campelo, Lucas S. Batista, Claus Aranha:
The MOEADr Package - A Component-Based Framework for Multiobjective Evolutionary Algorithms Based on Decomposition. CoRR abs/1807.06731 (2018) - [i2]Felipe Campelo, Fernanda C. Takahashi:
Sample size estimation for power and accuracy in the experimental comparison of algorithms. CoRR abs/1808.02997 (2018) - [i1]Athila R. Trindade, Felipe Campelo:
Tuning metaheuristics by sequential optimization of regression models. CoRR abs/1809.03646 (2018) - 2017
- [c18]Rafael Frederico Alexandre, Felipe Campelo
Multiobjective evolutionary algorithms for operational planning problems in open-pit mining. GECCO (Companion) 2017: 259-260 - [c17]Fillipe Goulart
Robust multiobjective optimization using regression models and linear subproblems. GECCO 2017: 569-576 - 2016
- [j4]Fillipe Goulart
Preference-guided evolutionary algorithms for many-objective optimization. Inf. Sci. 329: 236-255 (2016) - [c16]Felipe Campelo
Experimental Investigation of Recombination Operators for Differential Evolution. GECCO 2016: 221-228 - [c15]Mateus A. M. Teixeira, Fillipe Goulart
Evolutionary Multiobjective Optimization of Winglets. GECCO 2016: 1021-1028 - 2015
- [c14]Rafael Frederico Alexandre, Felipe Campelo
A Comparative Study of Algorithms for Solving the Multiobjective Open-Pit Mining Operational Planning Problems. EMO (2) 2015: 433-447 - 2014
- [j3]A. R. R. Freitas, V. M. R. Silva, Felipe Campelo
Optimizing two-level reverse distribution networks with hybrid memetic algorithms. Optim. Lett. 8(2): 753-762 (2014) - [c13]André L. Maravilha
Combinatorial optimization with differential evolution: a set-based approach. GECCO (Companion) 2014: 69-70 - [c12]Rodolfo Ayala Lopes, Rodrigo César Pedrosa Silva
A study on the configuration of migratory flows in island model differential evolution. GECCO (Companion) 2014: 1015-1022 - 2013
- [j2]Luciana Pereira de Assis
Multiobjective vehicle routing problem with fixed delivery and optional collections. Optim. Lett. 7(7): 1419-1431 (2013) - [c11]Andre Luis Silva, Jaime Arturo Ramírez
A statistical study of discrete differential evolution approaches for the capacitated vehicle routing problem. GECCO (Companion) 2013: 77-78 - [c10]Fillipe Goulart
Influence of relaxed dominance criteria in multiobjective evolutionary algorithms. GECCO (Companion) 2013: 89-90 - [c9]Rodolfo Ayala Lopes, Rodrigo César Pedrosa Silva, Felipe Campelo
Dynamic selection of migration flows in island model differential evolution. GECCO (Companion) 2013: 173-174 - 2012
- [c8]Rodolfo Ayala Lopes, Rodrigo César Pedrosa Silva
A Multi-agent Approach to the Adaptation of Migration Topology in Island Model Evolutionary Algorithms. SBRN 2012: 160-165 - [c7]A. R. R. Freitas, V. M. R. Silva, Frederico G. Guimarães
Genetic Algorithms Applied to Reverse Distribution Networks. SOCO 2012: 317-326 - 2011
- [c6]Lucas S. Batista
A comparison of dominance criteria in many-objective optimization problems. IEEE Congress on Evolutionary Computation 2011: 2359-2366 - [c5]Lucas S. Batista
Pareto Cone ε-Dominance: Improving Convergence and Diversity in Multiobjective Evolutionary Algorithms. EMO 2011: 76-90 - 2010
- [c4]Lucas S. Batista
A new self-adaptive approach for evolutionary multiobjective optimization. IEEE Congress on Evolutionary Computation 2010: 1-8
2000 – 2009
- 2007
- [j1]Frederico G. Guimarães
Design of mixed H2/Hinfinity control systems using algorithms inspired by the immune system. Inf. Sci. 177(20): 4368-4386 (2007) - [c3]Felipe Campelo
Overview of Artificial Immune Systems for Multi-objective Optimization. EMO 2007: 937-951 - 2006
- [c2]Frederico G. Guimarães, Elizabeth F. Wanner
Local Learning and Search in Memetic Algorithms. IEEE Congress on Evolutionary Computation 2006: 2936-2943 - [c1]Felipe Campelo
An Immune-based Algorithm for Topology Optimization. IEEE Congress on Evolutionary Computation 2006: 3204-3211
Coauthor Index
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