Last few months I was playing with Artificial Neural Network (ANN) and how to implement it in to the GPdotNET. ANN is one of the most popular methods in Machine Learning, specially Back Propagation algorithm. First of all, the Artificial Neural Network is more complex than Genetic Algorithm, and you need to dive deeper in to math and other related fields in order to understand some of the core concept of the ANN. But likely there are tons of fantastic learning sources about ANN. Here is my recommendation of ANN learning sources:
First of all there are several MSDN Magazine articles about ANN and how to implement it in C#.
If you want to know what’s behind the scene of ANN, read this fantastic online book with great animations of how neuron and neural networks work.
1. Neural Networks and Deep Learning, by Michael Nielsen.
There is a series you tube video about ANN.
Open source libraries about ANN in C#:
1. AForge.NET. – Computer Vision, Artificial Intelligence, Robotics.
The first GPdotNET v4.0 beta will be out very soon.
The WordPress.com stats helper monkeys prepared a 2014 annual report for this blog.
Here’s an excerpt:
The Louvre Museum has 8.5 million visitors per year. This blog was viewed about 79,000 times in 2014. If it were an exhibit at the Louvre Museum, it would take about 3 days for that many people to see it.
This is updated blog post about GPdotNET citation
Originally posted on Bahrudin Hrnjica Blog:
Recently I have googled about GPdotNET to find out how people use GPdotNET. I was surprised that there are plenty of sites which are published GPdotNET as freeware software. I have also found several scientific papers which citated GPdotNT as well. Some other people used it as elegant example in their lessons. Some students used GPdotNET in seminars and diploma works, master and phd thesis.
All in all I was very excited about it. So lets list some interested web sites and scientific paper which mentioned GPdotNET.
3. Genetic Programming article: http://www.answers.com/topic/genetic-programming
4. Teaching lessons from Faculty of Informatics, Burapha University, Thailand: Evolutionary Algorithms Applied to Finance
5. Karlsruhe Institute of Technology Paper Work: Evolutionary Algorithms.
6. Scientific paper, ACS Vol.14: Use of Learning Methods to Improve Kinematic Models
7. Scientific paper, JPE Vol.15: Modeling of Discharge Energy in Electrical Discharge…
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As currently implemented GPdotNET has classic crossover implementation without any intelligent way to exchange genetic materials. In most time classic crossover operation is destructive operation wasting lot of good genetic materials. By including brood recombination crossover can be slightly improved.
Brood recombination simple repeats crossover operation several time on the same parents, with different crossover points. After fitness evaluation of offspring, the best two child are kept and others are discarded. On that way there is a better chance to get better child than with classic crossover. The picture below graphically describes brood recombination.
Brood Size – new GPdotNET parameter
The first feature which will be implemented is manually setting the Brood Size of crossover. By adding Brood Recombination, we will increase possibility that two chromosomes will exchange the best genetic material they have.
The next feature will be brood size which will be generating dynamically and will be dependent of the generation number.