Title
Darwin-less Evolutionary Algorithms: Less Randomness, More Intelligence
Abstract
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”.
Year
Venue
Keywords
2009
GEM
evolutionary algorithm
Field
DocType
Citations 
Memetic algorithm,Evolutionary algorithm,Computational intelligence,Human-based evolutionary computation,Computer science,Evolutionary computation,Genetic programming,Artificial intelligence,Cultural algorithm,Evolutionary programming
Conference
0
PageRank 
References 
Authors
0.34
0
4