Tag: genetic algorithms

GELAB – A Matlab Toolbox for Grammatical Evolution

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We remember we wrote quite a lot about grammatical evolution. The last time we mentioned it, we said that grammatical evolution goes to Mathworks. We did not toot our horn much about it since then. We were busy in benchmarking this tool and we have been writing about it. We even renamed it to GELAB, as it is implemented in Matlab. A good thing that has happened recently is that GELAB is not published work. Yes, it is now available in lecture notes on artificial intelligence with Springer Verlag. You can peruse the article here.

GELAB – A Matlab Toolbox for Grammatical Evolution

In this paper, we present a Matlab version of libGE. libGE is a famous library for Grammatical Evolution (GE). GE was proposed initially in [1] as a tool for automatic programming. Ever since then,…

A nice presentation about GELAB can also be viewed below. We hope you like it!

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Grammatical Evolution Comes to Matlab

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You can work for a lifetime and not earn enough for yourself. You can spend a lifetime in service of humanity and you would not have served so well. But sometimes you spend a little bit of your time on something and do enough work that is sufficient for yourself, your community and even the future generations of humanity. Investing time in the development of software as useful as grammatical evolution (GE) is such a great thing. What researchers have achieved with GE is no secret and nothing sort of miraculous. And GE has been in C++ so far. This meant that a researcher would have to go through the pains of doing so many configurations before making it work. But GE comes to Matlab now. Recently we announced that GE came to Java. With a few tweaks, we can now run it in Matlab. What does this mean for us? This means that the genie is out of the bottle. Indeed if you look at the work that has been done through GE, you will begin to believe that it is the genie that came out of Aladin’s golden lamp. With its coming to Matlab, this means that the humanity has entered a new era of innovation, creativity, and development.

 

adilraja/libGEjava

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

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Grammatical Evolution Comes to Java

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We recently ported the renowned grammatical evolution (GE) algorithm to Java. GE is a strong optimization and machine learning (ML) algorithm that was conceived, invented and developed in Bio-Developmental Systems Research Group at University of Limerick, Ireland. For ages, it existed in C++, as that was the language of choice for its initial implementation. Now we have translated it into Java. We hope that this translation is going to be helpful in innovating many newer technologies.

adilraja/libGEjava

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

Grammatical evolution – IEEE Journals & Magazine

We present grammatical evolution, an evolutionary algorithm that can evolve complete programs in an arbitrary language using a variable-length binary strin

libGE

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Evolution of Mona Lisa With Pablo Picasso’s Paintings

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In the past, we have also been involved with evolutionary design. In particular, we tried to do an experiment to recreate Mona Lisa using a handful of paintings of Pablo Picasso. We wanted to see how it all goes. Our work was recently published in an IEEE outlet. The good thing about evolutionary design is that it is a rapidly thriving field with lots of applications in many feats. Please read our article as follows.

 

Evolution of Mona Lisa with Pablo Picasso’s paintings – IEEE Conference Publication

In recent years, design of artistic artifacts using machine learning (ML) techniques has become a major feat. Artificial creation of artistic artifacts inv

Also, please review our presentation of it on Youtube and Vimeo.

Evolution of Mona Lisa Using Pablo Picasso’s Paintings from Adil Raja on Vimeo.

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A Tutorial on Simulating Unmanned Aerial Vehicles

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We have been working on simulating autonomous drones for quite some time now. We published details of our work in the past. Recently, we thought about publishing our experiences with the feat of simulating drones.

Autonomous unmanned aerial vehicles (UAVs) have been a hot topic in academic circles lately. Enormous literature can be found on this subject. Approaches that address issues of developing planes that fly autonomously, without the intervention of a human pilot, as well as that also coordinate and cooperate with other planes are omnipresent. Academic literature is replete with such examples. The discipline can also be quite enticing. However, as it can be the case with any research endeavor, there are plenty of concerns that may remain obfuscated even after reviewing considerable literature.

Please review the rest of the article as follows.

A tutorial on simulating unmanned aerial vehicles – IEEE Conference Publication

This paper presents our reflections about our recent, intense involvement with the simulation of unmanned aerial vehicles (UAVs). Our idea was to integrate

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Evolving Technical Trading Strategies Using Genetic Algorithms: A Case About Pakistan Stock Exchange

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Last summer our team sat down together to come up with a strategy to predict stock market ups and downs. We employed genetic algorithms to predict stock price variations. In particular, our work was focused on Pakistan Stock Exchange. Our work was quite successful and was accepted to appear in Lecture Notes on Computer Science (LNCS), Springer. Please peruse our work here.

 

Evolving Technical Trading Strategies Using Genetic Algorithms: A Case About Pakistan Stock Exchange

Finding optimum trading strategies that maximize profit has been a human desire since the inception of the first stock market. Many techniques have been employed ever since to accomplish this goal…

Currently, we are still working in this domain with more rigor and vigor. We are trying with more sophisticated algorithms on larger datasets.

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