Avoiding premature convergence in estimation of distribution algorithms
Luis de la Ossa, José Antonio Gámez, Juan Luis Mateo, Jose Miguel Puerta · 2009
This work studies the problem of premature convergence due to the lack of diversity in Estimation of Distributions Algorithms. This problem is quite important for these kind of algorithms since, even when using very complex probabilistic models, they can not solve certain optimization problems such as some deceptive, hierarchical or multimodal ones.