Solving the Sports League Scheduling Problem with Tabu Search

In this paper we present a tabu approach for a version of the Sports League Scheduling Problem. The approach adopted is based on a formulation of the problem as a Constraint Satisfaction Problem (CSP). Tests were carried out on problem instances of up to 40 teams representing 780 integer variables with 780 values per variable. Experimental results show that this approach outperforms some existing methods and is one of the most promising methods for solving problems of this type

Quantized spin waves in the metallic state of magnetoresistive manganites

High resolution spin waves measurements have been carried out in ferromagnetic (F) La(1-x)(Sr,Ca)xMnO3 with x(Sr)=0.15, 0.175, 0.2, 0.3 and x(Ca)=0.3. In all q-directions, close to the zone boundary, the spin wave spectra consist of several energy levels, with the same values in the metallic and the x\approx 1/8 ranges. Mainly the intensity varies, jumping from the lower energy levels determined in the x\approx 1/8 range to the higher energy ones observed in the metallic state. On the basis of a quantitative agreement found for x(Sr)=0.15 in a model of ordered 2D clusters, the spin wave anomalies of the metallic state can be interpreted in terms of quantized spin waves within the same 2D clusters, embedded in a 3D matrix.Comment: 4 pages, 5 figure

Constraint-directed Search in Computational Finance and Economics

Constraints shield solutions from a problem solver. However, in the hands of trained constraint problem solvers, the same constraints that create the problems in the first place can also guide problem solvers to solutions. Constraint satisfaction is all about learning how to flow with the force of the constraints. Examples of using constraints to guide one’s search are abundant in complete search methods (e.g. see [1, 2]). Lookahead algorithms propagate constraints in order to (a) reduce the remaining problem to smaller problems and (b) detect dead-ends. Dependency-directed backtracking algorithms use constraints to identify potential culprits in dead-ends. This helps the search to avoid examining (in vain) combinations of variables assignments that do not matter. Constraint-directed search is used in stochastic search too. Constraints were used in Guided Local Search (GLS) [3] and Guided Genetic Algorithm (GGA) [4] to guide the search to promising areas of the search space. In stochastic methods, a constraint satisfaction problem is handled as an optimization problem, where the goal is to minimize the number of constraints violated. The approach i

A formalization of double auction market dynamics

Biographical notes on contributors: Edward Tsang has a first degree in Business Administration (Major in Finance) and a PhD in Computer Science. He has broad interest in applied artificial intelligence, in particularly computational finance, heuristic search, constraint satisfaction and scheduling. He is currently a professor in computer science at the University of Essex where he leads the Computational Finance Group and Constraint Satisfaction and Optimization Group. He is also the Director of the Centre for Computational Finance and Economic Agents (CCFEA), an interdisciplinary centre. He founded and chaired the Technical Committee for Computational Finance under the IEEE Computational Intelligence Society in 2004-2005. Richard Olsen has a Master in Economics from Oxford University and a PhD in law from the University of Zurich. He has specialized in high frequency finance and has been a pioneer of this discipline. In 1995, he co-organized the first conference in the field. In 2001, he and his team published a book, ‘Introduction to High Frequency Finance’, Academic Press. He is CEO of Olsen Ltd, a systematic asset management company based in Zurich and co-founde