One of the entries, titled “Towards Better than Human Capability in Diagnosing Prostate Cancer Using Infrared Spectroscopic Imaging“, was led by DITA and IlliGAL member Xavier Llorà. Other team members included Rohith Reddy, Brian Matesic, and Rohit Bhargava. You can download a technical report of the work here.

The other bronze humies award winning entry, titled “Automated Alphabet Reduction Method with Evolutionary Algorithms for Protein Structure Prediction“, was led by IlliGAL affiliate Jaume Bacardit and the team included DITA and IlliGAL members Xavier Llorà and Kumara Sastry. Other team members were Michael Stout, Jonathan D. Hirst, and Natalio Krasnogor. You can download a technical report of the work here.

IlliGAL members David E. Goldberg, Kumara Sastry and DITA member Xavier Llorà got awarded with best paper in the Estimation of Distribution Algorithms’ track in GECCO 2007 for their work entitled “Towards Billion Bit optimization via Efficient Genetic Algorithms”. You can download a technical report of the work can be downloaded here.

The push for better understanding and design of complex systems requires the solution of challenging optimization problems with large numbers of decision variables. This note presents principled results demonstrating the scalable solution of a difficult test function on instances over a billion variables using a parallel implementation of a genetic algorithm (GA). The problem addressed is a noisy, blind problem over a vector of binary decision variables. Noise is added equaling a tenth of the deterministic objective function variance of the problem, thereby making it difficult for simple hillclimbers to find the optimal solution. The genetic algorithm used - the compact GA - is able to find the optimum in the presence of noise quickly, reliably, and accurately, and the solution scalability follows known convergence theories. These results on noisy problem together with other results on problems involving varying modularity, hierarchy, and overlap foreshadow routine solution of billion-variable problems across the landscape of complexity science.