|
In computer science and machine learning, population-based incremental learning (PBIL) is an optimization algorithm, and an estimation of distribution algorithm. This is a type of genetic algorithm where the genotype of an entire population (probability vector) is evolved rather than individual members.〔 〕 The algorithm is proposed by Shumeet Baluja in 1994. The algorithm is simpler than a standard genetic algorithm, and in many cases leads to better results than a standard genetic algorithm. == Algorithm == In PBIL, genes are represented as real values in the range (), indicating the probability that any particular allele appears in that gene. The PBIL algorithm is as follows: # A population is generated from the probability vector. # The fitness of each member is evaluated and ranked. # Update population genotype (probability vector) based on fittest individual. # Mutate. # Repeat steps 1-4 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Population-based incremental learning」の詳細全文を読む スポンサード リンク
|