Core Algorithms for Multi-Objective Optimization
Project description
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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
moocore-0.1.1.tar.gz
(319.2 kB
view hashes)
Built Distributions
moocore-0.1.1-py3-none-win_amd64.whl
(440.1 kB
view hashes)
Close
Hashes for moocore-0.1.1-py3-none-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b0b3b2d2958f4af31ecf141b169ce257881045879c2d28ac89c19af8329ff011 |
|
MD5 | 17bbe55cf5c35e3bbc568babb19fb85d |
|
BLAKE2b-256 | f490b5415c97c7ce024e58411dd7e50955300e2d72217d66dd00483a80fa6472 |
Close
Hashes for moocore-0.1.1-py3-none-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | bf4fe584cc4d77793fa780f46d06417338b192a6a1c977fe4f17da004f37655f |
|
MD5 | d466bdc6a3d2b7a56e81033a5adf3223 |
|
BLAKE2b-256 | e42443d225b4007fc6e936115e36ce46c847921057328d637e9d605c6957f568 |
Close
Hashes for moocore-0.1.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d2821bb086c5efd6fccef42f307d8eed70d3627a7c9358dd2a321fdb804be660 |
|
MD5 | 8ce815ae679e980ef8af2678fe7d1f0e |
|
BLAKE2b-256 | 0866f0dd4f928e9631ecccc83ffb41e5f4d2c6e3808f4ad2be11f82da4b6f388 |
Close
Hashes for moocore-0.1.1-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f670d0a4422fbb7e238018bd415aeac2a09f1f132eb8684600ccf586ce38afdc |
|
MD5 | ea22d5d8e93cb92af93b6141b57b7fb3 |
|
BLAKE2b-256 | 96bea7c3d2d1bbae66d0b9bf6103fda54db97a9c879fb39debb30ee9e83abf7c |
Close
Hashes for moocore-0.1.1-py3-none-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1de635faea1a8f5560932596e1b1e87ea89747a0930c120ac1bf1e798bd12639 |
|
MD5 | 9dac935d58820bc07078a885414851c6 |
|
BLAKE2b-256 | 3c62c856906e80f4f98b9a8d4f3f562c48e615cd7d5e1e9ca343531a09f28257 |
Close
Hashes for moocore-0.1.1-py3-none-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c2b424ed069172386cf3af2ead8c37e23a7eb74952639304bff024b76601c6c6 |
|
MD5 | fae75fe539b3c66342be0b543daec266 |
|
BLAKE2b-256 | 769138a561f7e48fd4d8c0230b7a6cd9ec95f77031443c2ad10e039ebc903095 |
Close
Hashes for moocore-0.1.1-py3-none-macosx_10_9_universal2.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 281c5158b35834ab5643601831d09f21c83a1f65fc6f3cd7c43f037e19fd829b |
|
MD5 | 0ebc17e7a9bbb6ca00d0ef8aba6084e3 |
|
BLAKE2b-256 | 75c758753cefd146dcbc1bde710aaf598d52da30d820e35164a401074c407324 |