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Population-based incremental learning
open-in-new
<p>In <a href="/facts/Computer_science/lN3pEyPz">computer science</a> and <a href="/facts/Machine_learning/e0w0XJTu">machine learning</a>, population-based incremental learning (PBIL) is an <a href="/facts/Optimization_(mathematics)/oRn8Iv5I">optimization</a> <a href="/facts/Algorithm/fnl5NmRt">algorithm</a>, and an <a href="/facts/Estimation_of_distribution_algorithm/oeGEPIv4">estimation of distribution algorithm</a>. This is a type of <a href="/facts/Genetic_algorithm/WP2AFWuW">genetic algorithm</a> where the <a href="/facts/Genotype/MlzK1Mfz">genotype</a> of an entire population (<a href="/facts/Probability/fgYvMhwb">probability</a> <a href="/facts/Euclidean_vector/bCLrdksy">vector</a>) 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. </p>