Bayesian Study on When to Restart Heuristic Search

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian Study on When to Restart Heuristic Search

Popis výsledku v původním jazyce

Heuristic algorithm performance measures assess the quality of a search process by statistically analyzing its performance data, typically the number of objec- tive function evaluations before optimal or acceptable solution is found. Such criteria are not only intended to provide the verdict on which algorithm is better for what task, but also to help make the best possible use of a given algorithm on a given task. This target may be achieved by an appropriate restart strategy of the search process. In our paper we formulate axiomatic approach which also describes existing performance measures. Novelty of this paper consist in performance measure analysis via Marko- vian chain calculation and its direct Bayesian estimation based on Monte Carlo sim- ulations. Practical results are demonstrated on combinatorial optimization problems and are applicable e.g. to NP-hard problems from the field of operational research.

Název v anglickém jazyce

Bayesian Study on When to Restart Heuristic Search

Popis výsledku anglicky

Heuristic algorithm performance measures assess the quality of a search process by statistically analyzing its performance data, typically the number of objec- tive function evaluations before optimal or acceptable solution is found. Such criteria are not only intended to provide the verdict on which algorithm is better for what task, but also to help make the best possible use of a given algorithm on a given task. This target may be achieved by an appropriate restart strategy of the search process. In our paper we formulate axiomatic approach which also describes existing performance measures. Novelty of this paper consist in performance measure analysis via Marko- vian chain calculation and its direct Bayesian estimation based on Monte Carlo sim- ulations. Practical results are demonstrated on combinatorial optimization problems and are applicable e.g. to NP-hard problems from the field of operational research.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Mathematical Methods in Economics MME 2017

ISBN

978-80-7435-678-0

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

480-485

Název nakladatele

Univerzita Hradec Králové

Místo vydání

Hradec Králové

Místo konání akce

Hradec Králové

Datum konání akce

13. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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