Heterogeneous Island Models and Their Application to Recommender Systems and Electric Vehicle Charging

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10398560" target="_blank" >RIV/00216208:11320/20:10398560 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=j2N23X_KNw" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=j2N23X_KNw</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0218213020600106" target="_blank" >10.1142/S0218213020600106</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Heterogeneous Island Models and Their Application to Recommender Systems and Electric Vehicle Charging

Popis výsledku v původním jazyce

In this paper we describe a general framework for parallel optimization based on the island model of evolutionary algorithms. The framework runs a number of optimization methods in parallel with periodic communication, in this way, it essentially creates a parallel ensemble of optimization method. At the same time, the system contains a planner that decides which of the available optimization methods should be used to solve the given optimization problem and changes the distribution of such methods during the run of the optimization. Thus, the system effectively solves the problem online parallel portfolio selection. The proposed system is evaluated in a number of common benchmarks with various problem encodings as well as in two real-life problems -- the optimization in recommender systems and the training of neural networks for the control of electric vehicle charging.

Název v anglickém jazyce

Heterogeneous Island Models and Their Application to Recommender Systems and Electric Vehicle Charging

Popis výsledku anglicky

In this paper we describe a general framework for parallel optimization based on the island model of evolutionary algorithms. The framework runs a number of optimization methods in parallel with periodic communication, in this way, it essentially creates a parallel ensemble of optimization method. At the same time, the system contains a planner that decides which of the available optimization methods should be used to solve the given optimization problem and changes the distribution of such methods during the run of the optimization. Thus, the system effectively solves the problem online parallel portfolio selection. The proposed system is evaluated in a number of common benchmarks with various problem encodings as well as in two real-life problems -- the optimization in recommender systems and the training of neural networks for the control of electric vehicle charging.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GJ17-10090Y" target="_blank" >GJ17-10090Y: Optimalizace sítí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal on Artificial Intelligence Tools

ISSN

0218-2130

e-ISSN

—

Svazek periodika

29

Číslo periodika v rámci svazku

03n04

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

000563092500011

EID výsledku v databázi Scopus

2-s2.0-85086878301