Dr. Hamada Naoki

Senior Researcher, Artificial Intelligence Laboratory
Fujitsu Laboratories Ltd., Japan

Title: Evolutionary Multi-objective Optimization and Topological Data Analysis for Designing Innovative Products

Evolutionary Computation (EC) and Machine Learning (ML) are excellent tools to model, analyze and optimize various kinds of industrial problems. Since real-world problems often involve conflicting objectives to be optimized (e.g., cost vs. performance), we need to consider a trade-off between objectives. While such a trade-off surface can be obtained by using Evolutionary Multi-objective Optimization (EMO) algorithms, the dimensionality of the surface becomes higher as the number of objectives grows. Thus after optimization, additional analysis and visualization are required for understanding the trade-off. This talk introduces an emerging ML technique to analyze EMO outcomes: Topological Data Analysis (TDA), originated from...