Core Algorithms for Multi-Objective Optimization

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mlopezibanez

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Project description

moocore: Core Algorithms for Multi-Objective Optimization

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Contributors: Manuel López-Ibáñez, Fergus Rooney.

Introduction

The goal of moocore is to collect fast implementations of core mathematical functions and algorithms for multi-objective optimization. These functions include:

Identifying and filtering dominated vectors.
Quality metrics such as (weighted) hypervolume, epsilon, IGD, etc.
Computation of the Empirical Attainment Function. The empirical attainment function (EAF) describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space.

Keywords: empirical attainment function, summary attainment surfaces, EAF differences, multi-objective optimization, bi-objective optimization, performance measures, performance assessment

R package

There is also a moocore package for R: https://multi-objective.github.io/moocore/r

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Release history Release notifications | RSS feed

This version

0.1.1

Sep 5, 2024

0.0.9999

Jul 14, 2024

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