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Everything about Promoter based genetic algorithm

Genetic algorithm for neuroevolution

The promoter based genetic algorithm (PBGA) is a genetic algorithm for neuroevolution developed by F. Bellas and R.J. Duro in the Integrated Group for Engineering Research (GII) at the University of Coruña, in Spain. It evolves variable size feedforward artificial neural networks (ANN) that are encoded into sequences of genes for constructing a basic ANN unit. Each of these blocks is preceded by a gene promoter acting as an on/off switch that determines if that particular unit will be expressed or not. Wikipedia →

🧭 Start here: a 6-step reading path

Read the overview: Promoter based genetic algorithmA 5-minute orientation
Pick up a classic book: Neural Information ProcessingThe most-reprinted book on the subject
Read the landmark paper: Promoter recognition based on the Interpolated Markov Chains optimized via simulated annealing and genetic algorithmCited 2 times
Go sideways: Neuroevolution of augmenting topologiesA closely related topic
Go sideways: NeuroevolutionA closely related topic

📖 Definitions

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🗂️ The subject at a glance

🏆 Landmark papers (most cited)

Promoter recognition based on the Interpolated Markov Chains optimized via simulated annealing and genetic algorithm

Qiang Luo, Wenqiang Yang, Puyin Liu · 2006 · Pattern Recognition Letters · 2 citations

📚 Books worth reading

Neural Information ProcessingTingwen Huang · 2012
Principles of gene manipulation and genomicsS. B. Primrose · 2006
Intelligent Technologies and Engineering SystemsJengnan Juang · 2013
Advances in genetic programmingKenneth E. Kinnear · 1994
Applications of multi-objective evolutionary algorithmsCarlos A. Coello Coello, Gary B. Lamont · 2004
Advances in Brain Inspired Cognitive SystemsDerong Liu · 2013
📕Tech15projects.com · 2014
International encyclopedia of human geographyN. J. Thrift, Rob Kitchin · 2009

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

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