As presented in PyData London Meetup #58 (MAN, meetup.com)

Abstract:

When optimising for multiple factors, a common practice is to reduce the problem to 1D, which, because of the limited search space, is likely to result in a suboptimal solution. In Multi-Objective Optimisation we consider a set of trade-off solutions, called the Pareto Front, as the optimal set, and by so explore a multi-dimensional space, where we are more likely to find improved solutions. Motivated by the challenge to empirically navigate the vast DNA combinatorial sequence space to discover therapeutic proteins, I discuss Pareto Front optimisation using Evolutionary Algorithms. In particular, I highlight DEAP, a Python platform for prototyping.

Watch on YouTube, or here: