Traceable Evolutionary Algorithms (T-EA)
Tracing the evolution is almost impossible. This is due to the complexity of the operations (cross-over and mutation) over several generations. However, when it comes to evolutionary algorithms, it is of great interest to trace the impact of the solution candidates. For instance, when we use evolutionary algorithms to find the best optimal strategy to take a decision, we are very much interested to see which individual impacted the optimization process the most. In one of our recent works, Traceable Evolutionary Algorithms (T-EA), we developed new ideas for this purpose. The figure shows 20 individuals of T-EA. Left figure illustrates the fitness values of the first generation (the larger the better) and the right figure shows the influence of the individuals over 20 generations. We can trace back that only 4 individuals 2, 3, 6 and 11 (shown also with arrows) and the mutation operator had an impact:
More information can be found here:
- Cristian Ramirez-Atencia, Tobias Benecke and Sanaz Mostaghim
- T-EA: A Traceable Evolutionary Algorithm
- In the Proceedings of IEEE Congress on Evolutionary Computation (CEC), World Congress on Computational Intelligence, Glasgow, UK, 2020 DOI: 10.1109/CEC48606.2020.9185615 --> Link to video presentation, Link to IEEE Xplore