• Source: Population-based incremental learning

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 [0,1], 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




Source code


This is a part of source code implemented in Java. In the paper, learnRate = 0.1, negLearnRate = 0.075, mutProb = 0.02, and mutShift = 0.05 is used. N = 100 and ITER_COUNT = 1000 is enough for a small problem.




See also


Estimation of distribution algorithm (EDA)
Learning Classifier System (LCS)




References

Kata Kunci Pencarian:

• Population-based incremental learning
• Estimation of distribution algorithm
• Outline of machine learning
• Learning classifier system
• Reinforcement learning
• Deep learning
• Learning curve
• Michèle Sebag
• Advanced Personalized Learning
• List of datasets for machine-learning research