The relationship between learning and evolution in static and dynamic environments
Miguel Rocha, Paulo Cortez, José Neves · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2000
Evolution and lifetime learning have been adopted by living creatures to get the best of the adaptation processes to natural environments. Within the Machine Learning (ML) arena such methods have been treated, particularly in the fields of Genetic and Evolutionary Computation and Artificial Neural Networks. Why not to combine both techniques, giving rise to several ML models, namely those based on Lamarckian or Baldwinian approaches? The results so far obtained point to better performances with the former ones under static settings, but reward the latter under dynamic environments, where the learning tasks change over time.