Discovery of Innovative Knowledge through Optimization and Machine Learning.

Objective

• The aim is to develop a learning mechanism that can learn some problem-specific knowledge while solving an optimization problem using an EMO (Evolutionary Multiobjective Optimization) Algorithm through Machine Learning.
• This project aims to develop a model which could learn to improve the existing location of a point in 30 dimensional space over different generations for a continuous distribution of points.

Test Problem

There are various problems available on the pymoo framework for testing.

Description

Many models were tested using catboostregressor, SVRs, decision tree ensemble methods including random forests, gradient boost and xgboost in different hyperparameter settings. After testing models on different problems it was observed that random forest was giving appreciable results as compared to the original model.

(Complete details are not shared as paper has not been published yet and more work is going on.)