Cellular estimation bayesian algorithm for discrete optimization problems

Abstract:

In this paper, a new Cellular Estimation Bayesian Algorithm for discrete optimization problems is presented. This class of stochastic optimization algorithm with learning from the structure and parameters of local populations are based on independence test and decentralized populations scheme, which can reduce the number of function evaluations solving for discrete optimization problems. The experimental results showed that this proposal reduces the number of evaluations in the search of the optimal for a benchmark discrete function with respect to other approaches of the literature. Also, it achieved better performance than them.

Año de publicación:

2020

Keywords:

• Learning
• evolutionary algorithm
• Bayesian networks
• Cellular EDAs

Fuente:

scopus