Abstract. Many evolutionary algorithm applications involve either fitness functions with high time complexity or large dimensionality (hence very many fitness evaluations will typi...
Evolutionary Algorithms’ (EAs’) application to real world optimization problems often involves expensive fitness function evaluation. Naturally this has a crippling effect on ...
We use reconfigurable hardware to construct a high throughput Bayesian computing machine (BCM) capable of evaluating probabilistic networks with arbitrary DAG (directed acyclic gr...
In this work we present a novel and efficient algorithm– independent stopping criterion, called the MGBM criterion, suitable for Multiobjective Optimization Evolutionary Algorit...
This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...