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Evolutionary Computation in Continuous Optimization and Machine Learning
Mälardalen University, School of Innovation, Design and Engineering.
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

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 mathematical 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. 

Place, publisher, year, edition, pages
2017. , p. 38
Keyword [en]
evolutionary, algorithm, optimization
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-35674OAI: oai:DiVA.org:mdh-35674DiVA, id: diva2:1107808
Subject / course
Computer Science
Supervisors
Examiners
Available from: 2017-06-21 Created: 2017-06-11 Last updated: 2018-01-13Bibliographically approved

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