| Searching for common patterns on protein sequences by means of a parallel hybrid honey-bee mating optimization algorithm. | 0 | 0.34 | 2018 |
| A hybrid MPI/OpenMP parallel implementation of NSGA-II for finding patterns in protein sequences. | 0 | 0.34 | 2017 |
| A Comparative Study of Different Motif Occurrence Models Applied to a Hybrid Multiobjective Shuffle Frog Leaping Algorithm. | 0 | 0.34 | 2016 |
| Hybrid Multiobjective Artificial Bee Colony for Multiple Sequence Alignment | 17 | 0.59 | 2016 |
| Finding Patterns in Protein Sequences by Using a Hybrid Multiobjective Teaching Learning Based Optimization Algorithm | 0 | 0.34 | 2015 |
| A Parallel Multiobjective Approach based on Honey Bees for Traffic Grooming in Optical Networks | 1 | 0.37 | 2015 |
| Parallel H4MSA for Multiple Sequence Alignment | 1 | 0.35 | 2015 |
| Multiobjective swarm intelligence for the traffic grooming problem | 0 | 0.34 | 2015 |
| Parallelism in bioinformatics: A view from different parallelism-based technologies. | 0 | 0.34 | 2015 |
| Multiobjective optimization algorithms for motif discovery in DNA sequences | 0 | 0.34 | 2015 |
| An improved multiobjective approach inspired by the flashing behaviour of fireflies for Traffic Grooming in optical WDM networks. | 2 | 0.36 | 2014 |
| Parallelizing and optimizing a hybrid differential evolution with Pareto tournaments for discovering motifs in DNA sequences | 2 | 0.39 | 2014 |
| Designing a fine-grained parallel differential evolution with Pareto tournaments for solving an optical networking problem | 1 | 0.35 | 2014 |
| Performance assessment of multiobjective approaches in optical Traffic Grooming. | 1 | 0.36 | 2014 |
| Convergence analysis of some multiobjective evolutionary algorithms when discovering motifs | 1 | 0.35 | 2014 |
| The software project scheduling problem: A scalability analysis of multi-objective metaheuristics. | 24 | 0.84 | 2014 |
| Designing a novel hybrid swarm based multiobjective evolutionary algorithm for finding DNA motifs | 1 | 0.36 | 2013 |
| A Multiobjective SFLA-Based Technique for Predicting Motifs in DNA Sequences | 0 | 0.34 | 2013 |
| Hybrid multiobjective artificial bee colony with differential evolution applied to motif finding | 1 | 0.35 | 2013 |
| Parallelizing a hybrid multiobjective differential evolution for identifying cis-regulatory elements | 1 | 0.35 | 2013 |
| A parallel cooperative team of multiobjective evolutionary algorithms for motif discovery | 2 | 0.35 | 2013 |
| Analysing the scalability of multiobjective evolutionary algorithms when solving the motif discovery problem | 1 | 0.35 | 2013 |
| Comparing multiobjective swarm intelligence metaheuristics for DNA motif discovery | 7 | 0.52 | 2013 |
| Predicting DNA Motifs by Using Evolutionary Multiobjective Optimization | 14 | 0.53 | 2012 |
| Solving the reporting cells problem by using a parallel team of evolutionary algorithms. | 0 | 0.34 | 2012 |
| Comparing multiobjective artificial bee colony adaptations for discovering DNA motifs | 2 | 0.36 | 2012 |
| On the scalability of multi-objective metaheuristics for the software scheduling problem. | 2 | 0.39 | 2011 |
| Finding motifs in DNA sequences applying a multiobjective artificial bee colony (MOABC) algorithm | 9 | 0.49 | 2011 |
| Applying a multiobjective gravitational search algorithm (MO-GSA) to discover motifs | 5 | 0.39 | 2011 |
| A Multiobjective Variable Neighborhood Search for Solving the Motif Discovery Problem | 8 | 0.51 | 2010 |
| A Parallel Cooperative Evolutionary Strategy for Solving the Reporting Cells Problem | 2 | 0.40 | 2010 |
| Using a Parallel Team of Multiobjective Evolutionary Algorithms to Solve the Motif Discovery Problem | 2 | 0.37 | 2010 |
