07-19, 16:05–16:35 (Europe/Prague), North Hall
Pymoo is an open source python framework with state-of-the-art optimisation and post performance analysis capabilities. It provides an object oriented interface to solve constrained Single/Multi-Objective optimisation problems with a catalog of algorithms, customisations and post-optimisation evaluation functionalities. With additional features like Visualisation of optimal pareto-fronts, decision making, parallelization and customised sampling, Pymoo promises to be highly valuable for scalable optimisation solutions.
I will share a github repository with jupyter notebooks and datasets with the attendees, before the talk and have multiple hands on segments during the talk. The talk will take the users step-by-step through the sections mentioned below -
- What is an optimisation problem ?
- Core components of an optimisation problem ?
- Finding the right type of solution ?
- Identifying constraints, bounds and objectives
- Pymoo API - Object Oriented Interface, Parallelization, Visulization
- Hands on Example : a ML Model as an objective function, to find optimal input variables for a desired output variable
- Customisations using Pymoo : Termination, Crossover, Mutation etc
- A one-stop solution for Optimisation Problems
Pranjal is a Senior AI Scientist at Polymerize, Singapore building the world's first unified Material Informatics platform to accelerate R&D using AI. He works on developing predictive models, optimisation techniques curated for small experimental datasets, multi-stage processes in continuous development systems. His interests lie in making AI interpretable, logical and reasonable.