Core Algorithms for Multi-Objective Optimization
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moocore: Core Algorithms for Multi-Objective Optimization
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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