Illinois Genetic Algorithms Laboratory

The Oregon Daily Emerald reports that James Bean, noted operations researcher, genetic algorithmist, and inventor of the random keys technique for permutation and other combinatorial problems, is using genetic algorithms to help develop sustainable automobile technology in his new role as Dean of the Lundquist College of Business. The research was begun at Michigan in […]

Genetic algorithms researcher and IlliGAL alumnus, Jian-Hung Chen, recently received top honors in Taiwan’s computer science PhD dissertation competition. His thesis, entitled Theory and Applications of Efficient Multi-Objective Evolutionary Algorithms (CS, Feng Chia University, 2004) was ranked number one among all CS dissertations in a national competition held under the auspices of the Institute of […]

Jason Lohn of the NASA Ames Research Center outside Mountain View, CA used a genetic algorithm to design a wide-beam space antenna. The corkscrew shaped antenna, small enough to fit in a wine glass, proved worthy to transmit the needed bandwidth. Lohn expects the antenna to take part in the NASA Space Technology 5 mission […]

Since many of us use C/C++ to code our GAs, and then use Matlab to plot the results, here’s several simple steps that allow you to call Matlab functions directly from C/C++:
1. Convert the main to mexFunction
mexFunction is the entrance of matlab executable.
mexFunction(int nlhs,mxArray *plhs[],int nrhs,const mxArray *prhs[]) {
int i;
[…]

Interactive evolutionary computation (IEC) (concretely IGA, IES, IEP, or IGP) is a framework for optimizing the target system based on IEC user’s subjective evaluation. The IEC user’s psychological evaluation scale in mind is reflected in his or her subjective evaluation characteristics, and these in turn, are reflected in the systems designed with the IEC. We […]

Several years ago, Dave Goldberg lent me a little book by Nobel laureate Herbert A. Simon entitled The Sciences of the Artificial. I found this book really exciting. One aspect of this work directly relates to genetic and evolutionary algorithms… Herb Simon talks about decomposability of most complex real-world systems, which is a feature that […]

My turn I thought I would contribute with something simple but very useful for running scalability experiments.
As many know, I’ve worked on the design of better genetic algorithms (GAs) for quite a long time. I focused and still focus on designing GAs that scale up well, that means, their computational complexity should grow as […]

Matlab doesn’t have any direct function to plot 3D bars with error bars. Here is my script to do it, hope its useful for others as well.
clear;
g = [1 6 8 10:13 26 51 101]; % labels for x values
ny = 32; % Number of y values
nID = [1:10]; % Actual labels required on the […]

Usually, when I work on data mining problems using genetics-based machine learning, I tend to compare the results with the ones obtained using non evolutionary methods. I know Martin and Jaume have also been using some of this tools too in their data mining related papers.
The first one I started using was WEKA. It has […]

Martin Pelikan’s book titled “Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms” is now available. In his book, Martin presents a principled method for designing and analyzing scalable genetic algorithms that can solve boundedly-difficult, hierarchically-decomposable problems in polynomial (usually sub-quadratic) time.
More details regarding hBOA and to download the software visit the hBOA […]