IMC: Difference between revisions
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Prototype a 2D visualization for an execution of a genetic algorithm (GA) applied to a multi-dimensional search problem. The visualization should provide insights into all stages of GA (selection, crossover, mutation) as well as into evolution of candidate solutions. We can assume that number of dimensions is between 10 and 50, number of candidate solutions (population size) is 500 and number of generations is around 100. The visualization should make it easy to understand internal workings of GA and to show an impact of different selection and crossover strategies or mutation rates. Students can use any general GA implementation and can apply it to any multi-dimensional search problem. | Prototype a 2D visualization for an execution of a genetic algorithm (GA) applied to a multi-dimensional search problem. The visualization should provide insights into all stages of GA (selection, crossover, mutation) as well as into evolution of candidate solutions. We can assume that number of dimensions is between 10 and 50, number of candidate solutions (population size) is 500 and number of generations is around 100. The visualization should make it easy to understand internal workings of GA and to show an impact of different selection and crossover strategies or mutation rates. Students can use any general GA implementation and can apply it to any multi-dimensional search problem. | ||
Taylan Toygarlar <Taylan.Toygarlar@imc.nl> | Taylan Toygarlar <Taylan.Toygarlar@imc.nl> | ||
Radmilo Racic <radmilo.racic@imc.nl> | Radmilo Racic <radmilo.racic@imc.nl> | ||
Revision as of 15:52, 21 October 2015
Contact: Liselot de Jonge <liselot.dejonge@imc.nl>
2016 proposals
earlier ideas
Maksym Korotkiy Maksym Korotkiy <Maksym.Korotkiy@imc.nl>
Prototype a 2D visualization for an execution of a genetic algorithm (GA) applied to a multi-dimensional search problem. The visualization should provide insights into all stages of GA (selection, crossover, mutation) as well as into evolution of candidate solutions. We can assume that number of dimensions is between 10 and 50, number of candidate solutions (population size) is 500 and number of generations is around 100. The visualization should make it easy to understand internal workings of GA and to show an impact of different selection and crossover strategies or mutation rates. Students can use any general GA implementation and can apply it to any multi-dimensional search problem.
Taylan Toygarlar <Taylan.Toygarlar@imc.nl>
Radmilo Racic <radmilo.racic@imc.nl>
Visualization techniques for large set of financial markets data This project develops techniques for visualizing multiple data sets of financial data, including ticker states of global futures and significant stocks, bonds and currencies. The end goal is not only to unearth hidden relationships and correlations between global markets but also to convey trader sentiment and pin point market moving trades. As global market landscape is quite complex and correlated, we will be using Oculus Rift as the principal display tool. Students will be provided with data from Eurostoxx, DAX, CAC, KOSPI, Nikkei, ES, EUR/USD, T-Note, GBL, etc.
