CK automation actions to let users implement portable, customizable and reusable program workflows for reproducible, collaborative and multi-objective benchmarking, optimization and SW/HW co-design:

Multiobjective active learning with tunable accuracy/efficiency tradeoff and clear stopping criterion.

Implementation of NSGA-II in Python

Multiple-criteria decision-making (MCDM) with Electre, Promethee, Weighted Sum and Pareto

A Python-based, open source, desktop application to import, read and analyse excel files in order to create and display multiple charts.

Advanced choice modeling with multidimensional utility representations.

Python bindings for OptFrame C++ Functional Core

Source code for the paper "Toward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement Learning"

Multi-Objective Multi-Agent RL with non-linear utility functions

Pareto (Type I) distribution standard deviation.

Pareto (Type I) distribution variance.

Pareto (Type I) distribution expected value.

Pareto (Type I) distribution excess kurtosis.

(Completed) Machine Learning and Multi-Objective Evolutionary Algorithms to Solve Real World Engineering Problems (MultiObjectiveOptimisation and ML)

Pareto (Type I) distribution skewness.

Pareto (Type I) distribution mode.

Pareto (Type I) distribution differential entropy.

A tool for automated in-silicio quantum multi objective optimization.

Pareto (80/20) analysis in Python—identifies the few categories driving most issues; reproducible script + chart for business prioritization.