Research in computational intelligence is directed toward building thinking machines and
improving our understanding of intelligence. As evident, the ultimate achievement in this …

In this paper the problem of critical node detection (CNDP) is approached using population-
based incremental learning (an estimation of distribution algorithm) and simulated …

In this paper we propose a new probability update rule and sampling procedure for
population-based incremental learning. These proposed methods are based on the concept …

The optimization of hyperparameters in Deep Neural Networks is a critical task for the final
performance, but it involves a high amount of subjective decisions based on previous …

Abstract Model-based optimization using probabilistic modeling of the search space is one
of the areas where research on evolutionary algorithms (EAs) has considerably advanced in …

In this paper we obtain bounds on the probability of convergence to the optimal solution for
the compact genetic algorithm (cGA) and the population based incremental learning (PBIL) …

Abstract Population-Based Incremental Learning (PBIL) algorithm is a combination of
evolutionary optimization and competitive learning derived from artificial neural networks …

This paper describes the adaptive approach of the Population-based Incremental Learning
(PBIL) algorithm, and proposes several Learning Rules aimed to improve its performance …

The application of a multi-population based Population-Based Incremental Learning (PBIL)
to power system controller design is presented in this paper. PBIL is a combination of …

Abstract Population-Based Incremental Learning (PBIL) is a combination of Genetic
Algorithm with competitive learning derived from Artificial Neural Network. It has recently …