Turku Centre for Computer Science

About: Turku Centre for Computer Science is a facility organization based out in Turku, Finland. It is known for research contribution in the topics: Decidability & Word (group theory). The organization has 382 authors who have published 1027 publications receiving 19560 citations.

A priori power measures and the institutions of the European Union

Abstract: The a priori voting powers of member countries in the council of ministers of the European Union have been discussed in the literature mainly from the view-point of Banzhaf and Shapley-Shubik indices. This paper discusses – in the light of these and other more recent (Colomer's, Holler's as well as Deegan and Packel's) power indices – the interaction of the council ministers and the European Parliament (EP) under the assumption that the consent of both bodies is needed to carry a motion or piece of union-wide legislation. Moreover, the double-majority principle is discussed from the view-point of voting power distribution. Finally we consider the voting power distribution in a three-chamber system consisting of Commission, Council and EP.

An adaptive hybrid genetic algorithm for the three-matching problem

Abstract: This paper presents a hybrid genetic algorithm (GA) with an adaptive application of genetic operators for solving the 3-matching problem (3MP), an NP-complete graph problem. In the 3MP, we search for the partition of a point set into minimal total cost triplets, where the cost of a triplet is the Euclidean length of the minimal spanning tree of the three points. The problem is a special case of grouping and facility location problems. One common problem with GA applied to hard combinatorial optimization, like the 3MP, is to incorporate problem-dependent local search operators into the GA efficiently in order to find high-quality solutions. Small instances of the problem can be solved exactly, but for large problems, we use local optimization. We introduce several general heuristic crossover and local hill-climbing operators, and apply adaptation to choose among them. Our GA combines these operators to form an effective problem solver. It is hybridized as it incorporates local search heuristics, and it is adaptive as the individual recombination/improvement operators are fired according to their online performance. Test results show that this approach gives approximately the same or even slightly better results than our previous, fine tuned GA without adaptation. It is better than a grouping GA for the partitioning considered. The adaptive combination of operators eliminates a large set of parameters, making the method more robust, and it presents a convenient way to build a hybrid problem solver.

Reaction System Models for the Heat Shock Response

Abstract: Reaction systems are a formal framework for modeling processes driven by biochemical reactions. They are based on the mechanisms of facilitation and inhibition. A main assumption is that if a resource is available, then it is present in sufficient amounts and as such, several reactions using the same resource will not compete concurrently against each other; this makes reaction systems very different as a modeling framework than traditional frameworks such as ODEs or continuous time Markov chains. We demonstrate in this paper that reaction systems are rich enough to capture the essential characteristics of ODE-based models. We construct a reaction system model for the heat shock response in such a way that its qualitative behavior correlates well with the quantitative behavior of the corresponding ODE model. We construct our reaction system model based on a novel concept of dominance graph that captures the competition on resources in the ODE model. We conclude with a discussion on the expressivity of reaction systems as compared to that of ODE-based models.