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Evolutionary algorithms in statistical learning: Automating the optimization procedure
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Evolutionära algoritmer i statistisk inlärning : Automatisering av optimeringsprocessen (Swedish)
Abstract [en]

Scania has been working with statistics for a long time but has invested in becoming a data driven company more recently and uses data science in almost all business functions. The algorithms developed by the data scientists need to be optimized to be fully utilized and traditionally this is a manual and time consuming process. What this thesis investigates is if and how well evolutionary algorithms can be used to automate the optimization process.

The evaluation was done by implementing and analyzing four variations of genetic algorithms with different levels of complexity and tuning parameters. The algorithm subject to optimization was XGBoost, a gradient boosted tree model, applied to data that had previously been modelled in a competition.

The results show that evolutionary algorithms are applicable in finding good models but also emphasizes the importance of proper data preparation.

Abstract [sv]

Scania har länge jobbat med statistik men har på senare år investerat i att bli ett mer datadrivet företag och använder nu data science i nästan alla avdelningar på företaget. De algoritmer som utvecklas av data scientists måste optimeras för att kunna utnyttjas till fullo och detta är traditionellt sett en manuell och tidskrävade process. Detta examensarbete utreder om och hur väl evolutionära algoritmer kan användas för att automatisera optimeringsprocessen.

Utvärderingen gjordes genom att implementera och analysera fyra varianter avgenetiska algoritmer med olika grader av komplexitet och trimningsparameterar. Algoritmen som var målet för optimering var XGBoost, som är en gradient boosted trädbaserad modell. Denna applicerades på data som tidigare hade modellerats i entävling.

Resultatet visar att evolutionära algoritmer är applicerbara i att hitta bra modellermen påvisar även hur fundamentalt det är att arbeta med databearbetning innan modellering.

Place, publisher, year, edition, pages
2019. , p. 96
Keywords [en]
evolutionary algorithms, statistical learning, gradient boosting, automation, artificial intelligence
Keywords [sv]
evolutionära algoritmer, statistisk inlärning, gradient boosting, automation, artificiell intelligens
National Category
Mathematics
Identifiers
URN: urn:nbn:se:umu:diva-160118OAI: oai:DiVA.org:umu-160118DiVA, id: diva2:1324166
External cooperation
Scania CV AB
Educational program
Master of Science in Engineering and Management
Supervisors
Examiners
Available from: 2019-06-14 Created: 2019-06-13 Last updated: 2019-06-14Bibliographically approved

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