The evolution of PBIL algorithm when used to solve the nuclear reload optimization problem

Genetic algorithms use the biological metaphor of the survival of the fittest proposed by Darwin to motivate its mechanism of individuals composing a population. Based on these concepts, Baluja has proposed the Population-Based Incremental Learning (PBIL), a stochastic optimization technique. This paper presents a review study related to the evolution of PBIL when used to optimize the nuclear reload process of the 7th operation cycle of the Brazilian PWR Angra 1. Since 1999, several researchers have been using the most varied optimization techniques which analyze this particular case study. Therefore, this survey aims to describe in which ways the different strategies adopted by some of PBIL's variations has contributed to obtain better results along the years on solving this particular problem, besides making a comparison of the results obtained by each of them.