Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Sauli Ruuska

dblp:90/10115 · DBLP profile ↗
← Back
3ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Mathematical optimization · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization
multi-objective optimization
0.212014
A solution process for simulation-based multiobjective design optimization with an application in the paper industry · Comput. Aided Des. 2014
Computational science and engineering › engineering design
simulation-based design
0.112014
A solution process for simulation-based multiobjective design optimization with an application in the paper industry · Comput. Aided Des. 2014

Methods — techniques the papers use, named apart from their topics

simulation-based optimization · 0.4multi-objective optimization · 0.4
YearPublicationVenuePosition
2014 A solution process for simulation-based multiobjective design optimization with an application in the paper industry
Ingrida Steponavice, Sauli Ruuska, Kaisa Miettinen
Comput. Aided Des.2
2012 Constructing evolutionary algorithms for bilevel multiobjective optimization
abstract
We propose a procedure to construct evolutionary bilevel optimization algorithms based on recent theoretical advances that have established connections between bilevel optimization and multiobjective optimization. In the proposed procedure, a new algorithm is defined by integrating an evolutionary multiobjective optimization algorithm with a partial order that is compatible with bilevel optimization. The advantages of the procedure include the ability to harness the methodology of evolutionary multiobjective optimization for bilevel optimization and to systematically develop new algorithms for single-objective and multiobjective bilevel optimization. No regularity assumptions are used, which ensures maximal applicability of the optimization algorithms constructed by the procedure. The necessary theoretical foundation is developed and the steps of the procedure are illustrated with an example.
Sauli Ruuska, Kaisa Miettinen
IEEE Congress on Evolutionary Computation1
2011 A new hybrid mutation operator for multiobjective optimization with differential evolution
Karthik Sindhya, Sauli Ruuska, Tomi Haanpää, Kaisa Miettinen
Soft Comput.2