Bachelor and Master Theses

Title: Evolutionary computation in continuous optimisation and machine learning
Subject: Computer Science
Level: Basic or Advanced (contact supervisor)
Description: Evolutionary computation is a field which uses natural computational processes to optimize mathematical and industrial problems. Differential Evolution, Particle Swarm Optimization and Estimation of Distribution Algorithm are some of the newer emerging varieties which have attracted great interest among researchers. This work has compared these three algorithms on a set of math- ematical and machine learning benchmarks and also synthesized a new algorithm from the three other ones and compared it to them. The results from the benchmark show which algorithm is best suited to handle various machine learning problems and presents the advantages of using the new algorithm. The new algorithm called DEDA (Differential Estimation of Distribution Algorithms) has shown promising results at both machine learning and mathematical optimization tasks.
Start date: 2017-03-20
Prel. end date: 2017-05-17
Presentation date: 2017-06-12
Student: Leslie Dahlberg ldg14001@student.mdh.se
IDT supervisor: Ning Xiong
ning.xiong@mdh.se, +46-21-151716
Examinator: Peter Funk
Peter Funk
peter.funk@mdh.se, +46-21-103153

Rapport och bilagor

Size

Senaste uppdatering

TR2007.pdf

3686978

2017-05-17, 02:14


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