Darwin-less Evolutionary Algorithms: Less Randomness, More Intelligence.
Felipe Houat de Brito, Artur Noura Teixeira, Otávio Noura Teixeira, Oliveira Júnior · GEM · 2009
For many years Evolutionary Techniques have been successfully applied in several computational optimization problems. In order for obtain “best results” and a wide exploration of the search surface, the choices for tuning those methods can be exponentially complex and require a large human intervention. Those traditional Darwinian models rely only on randomness without any specified objective. For that matter, the present work introduces the adoption of Intelligent Design Theory and the implementation of a Fuzzy Intelligent Designer agent, which dynamically control the algorithms parameters, adjusting their values for any given situation. These deliveries overexpectation results opening a wide new space for research: the “Darwin-less Evolutionary Algorithms”.