Computational Intelligence, Optimization and Evolutionary Computing

One of the backbones of our research is the application of the intelligent computing to real-world optimization problems in several domains, tackling the optimization of one or more than one objective (multiobjective optimization). Our group can make the most of its experience in many engineering problems using a wide set of intelligent computing methods: heuristics, metaheuristics, bio-inspired and evolutionary algorithms, etc. Metaheuristics are a family of techniques comprising Evolutionary Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Tabu Search, Scatter Search, Differential Evolution and others. Multiobjective optimization using metaheuristics started to gain interest in the 90s, mainly focused on the use of EAs: NSGA-II, SPEA2, PESA-II, PAES, AbYSS, etc.

Some of these problems are:

  • Routing and Wavelength Assignment.
  • Relay Node Placement in Wireless Sensor Networks for Energy Eficciency.
  • Radio Network Design by Efficient Base Station Placement.
  • Motif Discovery in DNA sequences.
  • Broadcasting in Metropolitan Mobile Ad Hoc Networks.
  • Ring Loading.
  • Reporting Cells.
  • Hybrid Face Recognition.
  • Time Series Identification.
  • Finding Deadlocks in Programs.
  • Fit of X-Ray Diffraction Peaks.
  • Logistic Curve Parameter Estimation.
  • Optimal Design of Cantilever Wall.
  • Placement and Routing of Boolean Functions in FPGAs.
  • ...



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