Recently I committed and pushed a new version of GELAB to its repository on GitHub. Since GELAB’s version is based on forces of nature, this third version is named Cyclone. It has nice features. The main thing is that it is compatible with Matlab R2023b. The Java code is also compiled with the latest versions of JDK and JVM. Any aspiring student or a practitioner of machine learning will like it. Grammatical evolution has many exciting applications and they are cited very well in existing literature. The added benefit of GELAB is that it is a lot more user-friendly. This owes to Matlab being a high-level scripting language. Implementing code and desirable use cases in Matlab is a breeze. It is already quite popular because of this feature in various engineering disciplines. GELAB can also be used in conjunction with other toolboxes of Matlab. And it also exploits the parallel programming toolbox if a multi-core system is available. Please have fun as you use GELAB. Please let me know if there are any bugs or errors.

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.

Analysis of Diversity in Grammatical Evolution – IEEE Conference Publication

Diversity is a much sought after aspect of any evolutionary system. More diversity means a cornucopia of diverse behaviors and traits among the individuals of a population. Lack of diversity, on the other hand, leads to a stagnant population whose individuals are more or less similar to each other.

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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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Evolving MIMO multi-layered artificial neural networks using grammatical evolution

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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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Adil Raja / irga

GitLab.com

As we reported in the associated article, this can be used to address theoretical questions about GAs, such as understanding and validating schema theory.

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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

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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 – 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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Computational materials science is a hot domain that, broadly speaking, allows innovation in the field of materials science through computational methods. What inspires me most about this is the realistic simulation of new materials that could be used revolutionize some industries, such as renewable energy. I have been writing about solar simulations before in which I pointed at tools that could be used for simulation of solar energy panels. Coupled with machine learning techniques, realistic simulation software can help us achieve higher strides in the domain of materials science.

I have been fascinated by other simulators that can help us in novelty detection in materials science. One such tool is Quantum Espresso. This is based on density functional theory and allows simulation of newer materials that would be hard to invent by hand and experiment in a lab.

QUANTUMESPRESSO – Quantum ESPRESSO

Quantum ESPRESSO

Quantum Espresso is already being coupled with machine learning to create novel materials. Here are a few links to published work.

EVO-Evolutionary algorithm for crystal structure prediction – ScienceDirect

EVO successfully demonstrates its applicability to find crystal structures of the elements of the 3rd main group with their different spacegroups. For this we used the number of atoms in the conventional cell and multiples of it.

[1610.08106] Evolutionary optimization of PAW data-sets for accurate high pressure simulations

Abstract: We examine the challenge of performing accurate electronic structure calculations at high pressures by comparing the results of all-electron full potential linearized augmented-plane-wave calculations with those of the projector augmented wave (PAW) method.

I advocate about a theme that I believe is very suitable for innovation. In this theme, I argue about gluing domain-specific realistic simulators with machine learning algorithms. If you scroll back and read again my article you will find very vivid hints about this theme, even if you ignored them before. If you want to develop a profound understanding of this idea, please read the following article.

Simulators as Drivers of Cutting Edge Research – IEEE Xplore Document

Undertaking engineering research can be compounding for beginning graduate students and thwarting even for seasoned researchers. With a wealth of academic

The phone call from Dr. Ghauri made me a day.

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