Konstantinos Mavreas (FACTAS)
The secrets of the Moon rocks, a basic idea of remanent magnetization and how to approximate it with rational analysis
Ferromagnetic materials (like iron) have ”memory”! This is in the form of natural remanent magnetization. Paleomagnetic studies, take advantage of that ”memory” and try to understand the evolution of the magnetic field in our planet and other planets / planetary objects. In our case Moon rocks have ”memory” of an ancient global magnetic field that no longer exist. In the talk we will see how we can use rational approximation techniques to recover that ”memory” and why this is important!
Patryk Filipiak (ATHENA)
Proactive Evolutionary Algorithms for Dynamic Optimization Problems
Dynamic optimization problems are problems that change as time goes by, e.g. high-frequency financial portfolio optimization or robotic arm movement planning. They are often addressed with reactive evolutionary algorithms that continuously explore a search space looking for new optima and trace the ones that were found so far. Algorithms equipped with such a mechanism are always one step back with a dynamic environment, since they can only detect the changes that already happened. The proposed proactive paradigm alleviates that issue by exploiting a dedicated forecasting model that anticipates a future landscape based on past observations. As a result, algorithms that apply this scheme can get ready for the changes to come, e.g. by directing some individuals into future promising regions.