FnS: Time-complexity analysis of co-evolutionary algorithms as an adversarial optimisation approach

Add to your list(s) Download to your calendar using vCal

• Mario Hevia Fajardo
• Thursday 06 October 2022, 16:00-16:30
• LG23.

If you have a question about this talk, please contact Mirco Giacobbe.

Co-evolutionary algorithms are simple general-purpose optimisers used to solve complex problems for which no objective function for evaluating potential solutions is present or known. Instead, a payoff function is used, such that the objective value of a solution depends on actions of some adversary or adversaries. These algorithms use one or more populations of solutions that mimic natural co-evolution of species by iteratively applying evolutionary operators such as mutation, recombination, and selection to improve the current solutions. Because of the complex interactions that arise between the populations of solutions, these algorithms are poorly understood, and applications are often limited by pathological behaviour, such as loss of gradient and cyclic non-convergent behaviour.

It is an open challenge to develop a theory that can predict when co-evolutionary algorithms find solutions efficiently and reliably. This talk describes time-complexity analyses that have provided a better understanding of how co-evolutionary algorithms behave throughout the optimisation.

This talk is part of the Facts and Snacks series.

This talk is included in these lists:

• Facts and Snacks
• LG23

Note that ex-directory lists are not shown.