moocore: Core Algorithms for Multi-Objective Optimization#
Version: 0.1.5.dev0 (See What’s new)
Date Oct 31, 2024
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This webpage documents the moocore Python package. There is also a moocore R package.
The goal of the moocore project (multi-objective/moocore) is to collect fast implementations of core mathematical functions and algorithms for multi-objective optimization and make them available to different programming languages via similar interfaces. 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.
Most critical functionality is implemented in C, with the R and Python packages providing convenient interfaces to the C code.
Keywords: empirical attainment function, summary attainment surfaces, EAF differences, multi-objective optimization, bi-objective optimization, performance measures, performance assessment
The reference guide contains a detailed description of the functions, modules, and objects.
Detailed examples and tutorials.