Devising Technical Trading Rules for Pakistan Stock Exchange using Genetic Programming – IEEE Conference Publication

The efficient market hypothesis (EMH) suggests that a stock market behaves like a random walk which means that developing profitable trading rules and forecasting the trends would be impossible.

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GitHub – adilraja/GELAB: GELAB: A Matlab Toolbox for Grammatical Evolution

GELAB: A Matlab Toolbox for Grammatical Evolution. Contribute to adilraja/GELAB development by creating an account on GitHub.

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adilraja/libGEjava

libGEjava – A framework for Grammatical Evolution in Java. This is based on the original source code of GE in C++.

This version of libGE is simply a code-level translation of the original libGE that was written in C++. As a matter of fact, I initiated the translation in August, 2007. I even completed most of the translation at that time. However, for some good reasons, I had to abandon it. I reverted back to it a while ago and here we have a working piece of code. You can clone it and open it in NetBeans IDE. The idea now is to use it in a more productive way. I shall say more about it later on.

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On Losses, Pauses, Jumps and the Wideband E-Model – IEEE Xplore Document

There is an increasing interest in upgrading the EModel, a parametric tool for speech quality estimation, to the wideband and super-wideband contexts. The

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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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A student of mine spoke yesterday in a class about his passion to work with the Linux operating system. This rekindled my own interest about this niche as well.

A slight search on Google reveals that applications of EAs for evolving operating systems are not scarce. Plenty of articles can be found online that employ EAs to optimize some aspect of an operating system. There are approaches which employ EAs to design better process scheduling algorithms. As a matter of fact, much of the work concerning applications of EAs for development of operating systems is about scheduling algorithms. This is the impression I have at least. In any case I do not believe that EAs cannot be applied to solve or optimize other aspects of operating systems. I personally think that it would be very interesting to see and apply EAs to figure out better user interfaces. Similarly, EAs could be applied to solve other problems that could be inherent in a cloud OS, such as distribution of networking, computing and storage resources. Feats such as software defined networking can also benefit from EAs. This can give rise to whole new discipline in computer science that I would rather like to call evolvable operating systems.

http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5641890&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5641890

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A good training software could be the ECJ package. ECJ is an evolutionary computing software package written in Java. It has quite a few nice algorithms implemented in it. A few examples are evolution strategies, genetic programming, genetic algorithms and particle swarm optimization.

A good gaming engine could be the TORCS simulator. TORCS stands for the open racing car(s) simulator and is used by the famous “The Simulated Car Racing Competition”. Other gaming engines could be thought about in which you can make objects and they act in a smart way in some meaningful sense. Software simulators for physical objects and devices could be counted in the same category.

One nice software simulator is the UAV Playground. With the UAV Playground you can create various types of drone plane as software objects in the simulator and have them fly (in the same simulator of course).

Flying an object with a joystick in a simulator is one thing. Another important thing is to the objects (drones in this case) by themselves in an intelligent manner. This is the challenge.

As discussed above a training software like ECJ can be used to accomplish this goal. The training software and the simulation software have to be integrated in order to accomplish this, however.

The challenge is to write an application integration software that:

1. Integrates ECJ and UAV Playground.
2. It should be easy to create members in ECJ that correspond with the controllers of the drones UAV Playground. This is to say that the members of ECJ should have a representation that is amenable with the representation of the controllers of the drones.
3. The integration software should preferably be written in Java and/or Matlab.

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