Traceability in Evolutionary Learning
Evolutionary optimization and learning algorithms help to find an optimal solution for complex problems. In a nutshell, they work like this: We start with a random set of solutions to the problem and by recombining the good ones, try to iteratively get closer to the optimal solution. This process is very complex and similar to the natural evolution is not easily tracable. However, we are interested to trace the impact of the initial random set of solutions on the outcome. In one of our recent papers which will be presented at IEEE Congress on Evolutionary Computation 2021, we are getting one step closer to this traceablity.
- Tobias Benecke and Sanaz Mostaghim
- Tracking the Heritage of Genes in Evolutionary Algorithms
- Accepted at the IEEE CEC 2021