symbolic regression | Psyops Prime https://psyopsprime.com An Idea Log Wed, 18 Nov 2015 12:08:33 +0000 en-US hourly 1 https://wordpress.org/?v=7.0.2 https://creativecommons.org/licenses/by-nc-nd/4.0/ 99640787 Modelling the Effect of Packet Loss on Speech Quality: Genetic Programming Based Symbolic Regression https://psyopsprime.com/computer-networks/modelling-the-effect-of-packet-loss-on-speech-quality-genetic-programming-based-symbolic-regression/?utm_source=rss&utm_medium=rss&utm_campaign=modelling-the-effect-of-packet-loss-on-speech-quality-genetic-programming-based-symbolic-regression https://psyopsprime.com/computer-networks/modelling-the-effect-of-packet-loss-on-speech-quality-genetic-programming-based-symbolic-regression/#respond Wed, 18 Nov 2015 12:08:33 +0000 http://psyopsprime.xyz/?p=1171 I delivered the following couple of presentations sometime in 2006. The ideas presented could be used to do some further research.   Photo by diskychick

The post Modelling the Effect of Packet Loss on Speech Quality: Genetic Programming Based Symbolic Regression first appeared on Psyops Prime.]]> I delivered the following couple of presentations sometime in 2006. The ideas presented could be used to do some further research.

 

Photo by diskychick

The post Modelling the Effect of Packet Loss on Speech Quality: Genetic Programming Based Symbolic Regression first appeared on Psyops Prime.]]> https://psyopsprime.com/computer-networks/modelling-the-effect-of-packet-loss-on-speech-quality-genetic-programming-based-symbolic-regression/feed/ 0 https://creativecommons.org/licenses/by-nc-nd/4.0/ 1171 GPLAB Adapted https://psyopsprime.com/machine-learning/gplab-adapted/?utm_source=rss&utm_medium=rss&utm_campaign=gplab-adapted https://psyopsprime.com/machine-learning/gplab-adapted/#respond Wed, 18 Nov 2015 06:06:35 +0000 http://psyopsprime.xyz/?p=1164 During my PhD research, I leveraged quite a lot from symbolic regression through genetic programming. In turn I used GPLAB for doing a great deal

The post GPLAB Adapted first appeared on Psyops Prime.]]> During my PhD research, I leveraged quite a lot from symbolic regression through genetic programming. In turn I used GPLAB for doing a great deal of my work. GPLAB is a famous Matlab toolbox for genetic programming aimed at addressing classification and regression problems. It has been implemented by Sara Silva from Portugal. In its very essence, it is quite comprehensive and thorough. Moreover, due to the very fact that it is written in Matlab, it is quite user-friendly and adaptable as well. If you have a nice algorithm that you want to hybridize with it, you can easily do so by manipulating the m-files of Matlab.

I have been using GPLAB for quite a lot of years now, with my own journey with it starting in it as early as 2006. I made some tweaks to the code. Due to this my own copy of the code has evolved quite a lot during the years. I finally decided to put my copy of it on GitHub. Due to the fact that GPLAB is open source, I don’t think that Sara will have a problem with that. As a matter of fact, she should be happy both because another copy of GPLAB is online and that Sara is a nice person. You can find my copy of the code by linking on the link below. I have made appropriate attributions to it as well. You can fork it, clone it and use it as you wish.

GitHub – adilraja/GPLAB-Adapted: Basically it is the same GPLAB by Sara Silva. A few adaptations have been made!

Basically it is the same GPLAB by Sara Silva. A few adaptations have been made! – GitHub – adilraja/GPLAB-Adapted: Basically it is the same GPLAB by Sara Silva. A few adaptations have been made!

Details About my Tweaks to the Code:

Since I have been changing the code over the years, it would be nice to reflect on the changes I have made over the years.

  1. The code implements linear scaling as proposed by Maarten Keijzer to its squared error function.
  2. Lexicographic parsimony pressure as proposed by Sean Luke was implemented.
  3. A strategy for tournament selection proposed by Stephen Gustafson was implemented.
  4. The function set now contains unprotected functions that accept any inputs and assign infinities to the results with inadmissible inputs.

Links to the relevant papers are as follows.

Scaled Symbolic Regression – Genetic Programming and Evolvable Machines

Performing a linear regression on the outputs of arbitrary symbolic expressions has empirically been found to provide great benefits. Here some basic theoretical results of linear regression are reviewed on their applicability for use in symbolic regression.

 

http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1554780&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1554780

Click to access lexicographic.pdf

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The post GPLAB Adapted first appeared on Psyops Prime.]]> https://psyopsprime.com/machine-learning/gplab-adapted/feed/ 0 https://creativecommons.org/licenses/by-nc-nd/4.0/ 1164 Java Beagle https://psyopsprime.com/education/java-beagle/?utm_source=rss&utm_medium=rss&utm_campaign=java-beagle https://psyopsprime.com/education/java-beagle/#comments Wed, 17 Dec 2014 04:42:51 +0000 http://psyopsprime.meximas.com/?p=416 Using genetic programming to find solutions to real-world problems is one thing, implementing a framework for automatic programming is totally another. Java beagle is a

The post Java Beagle first appeared on Psyops Prime.]]> beagle puppies photoUsing genetic programming to find solutions to real-world problems is one thing, implementing a framework for automatic programming is totally another. Java beagle is a result of that curiosity. Java beagle is based on the beagle puppy software of the open beagle project. It is basically aimed at symbolic regression problems. It implements some nice algorithms such as lexicographic parsimony pressure, linear scaling and subtree caching. It is absolutely functional and tried and tested several times on various problems. It can be found here.

Photo by -5Nap-

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