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Journal of Graph algorithms and Applications 2011年第1期15卷 127-155页

作者： Chimani, Markus Gutwenger, Carsten Mutzel, Petra Wong, Hoi-Ming Algorithm Engineering Group Friedrich-Schiller-University Jena Germany Chair for Algorithm Engineering TU Dortmund Germany

Recently, we presented a new practical method for upward crossing mini- mization [8], which clearly outperformed existing approaches for drawing hier- archical graphs in that respect. The outcome of this method is an upward planar representation (UPR), a planarly embedded graph in which crossings are repre- sented by dummy vertices. However, straight-forward approaches for drawing such UPRs lead to quite unsatisfactory results. In this paper, we present a new algorithm for drawing UPRs that greatly improves the layout quality, leading to good hierarchal drawings with few crossings. We analyze its performance on well-known benchmark graphs and compare it with alternative approaches.

关键词： Benchmarking

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Measuring multimodal optimization solution sets with a view to multiobjective techniques

Measuring multimodal optimization solution sets with a view ...

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International Conference on EVOLVE

作者： Preuss, Mike Wessing, Simon Computational Intelligence Group Chair of Algorithm Engineering Department of Computer Science TU Dortmund Dortmund Germany

ISBN: (纸本)9783319011271

As in multiobjective optimization, multimodal optimization generates solution sets that must be measured in order to compare different optimization algorithms. We discuss similarities and differences in the requirements for measures in both domains and suggest a property-based taxonomy. The process of measuring actually consists of two subsequent steps, a subset selection that only considers 'suitable' points (or just takes all available points of a solution set) and the actual measuring. Known quality indicators often rely on problem knowledge (objective values and/or locations of optima and basins) which makes them unsuitable for real-world applications. Hence, we propose a new subset selection heuristic without such demands, which thereby enables measuring solution sets of single-objective problems, provided a distance metric exists.

关键词： Multiobjective optimization

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Approximating Pareto Fronts by Maximizing the S-Metric with an SMS-EMOA/Gradient Hybrid

Approximating Pareto Fronts by Maximizing the S-Metric with ...

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20th Belgian-Dutch Conference on Artificial Intelligence, BNAIC 2008

作者： Emmerich, Michael Deutz, Andrè Beume, Nicola Leiden University Leiden Institute for Advanced Computer Science 2333 CA Leiden Netherlands University of Dortmund Chair of Algorithm Engineering 44221 Dortmund Germany

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TORCS sensor noise removal and multi-objective track selection for driving style adaptation

TORCS sensor noise removal and multi-objective track selecti...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Mike Preuss Jan Quadflieg Günter Rudolph Chair of Algorithm Engineering Technische Universität Dortmund Germany

Two related problems with TORCS car racing competition controllers are approached here. At first, we demonstrate how to handle the 10% artificial sensor noise that made proper track segment recognition quite difficult for some controllers in 2010 (when the noise was introduced). This is successfully dealt with by a combination of averaging and regression. The presented solution copes well with the natural antagonism between accuracy and the produced time lag, meaning that controllers are enabled to use full sensor information despite noise. Secondly, we suggest a solution for the problem of selecting a minimal set of controller parameter configurations suitable for several different tracks by applying principles of multi-objective optimization. While a full multi-objective approach is unfeasible here, we investigate the conflict potential between objectives (in this case tracks) in order to remove the ones that are less problematic. Naturally, the second problem is much more interesting but can only be tackled if the first one is resolved.

关键词： Noise Noise measurement Optimization Polynomials Target tracking Correlation Games

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Optimizing Door Assignment in LTL-Terminals by Evolutionary Multiobjective algorithms

Optimizing Door Assignment in LTL-Terminals by Evolutionary ...

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Congress on Evolutionary Computation

作者： T. Bartz-Beielstein A. Chmielewski M. Janas B. Naujoks R. Scheffermann Chair of Algorithm Engineering and Systems Analysis University of Dortmund Dortmund Germany

In less-than-truckload terminals arriving trucks have to be allocated to a gate and to a time slot for unloading. The allocation to a specific gate results in different transportation volumes for the forklift trucks inside of the terminal, depending on the destinations of the truck’s loads. While minimizing these transports the time for trucks waiting to be ordered to a gate also has to be minimized. For the first time this problem has been tackled as a 2-objective optimization problem and was solved by an (1+1)-evolution strategy. We developed a model which is derived from real freight forwarder’s data and represents a small company’s terminal on an average workday.

关键词： Transportation Genetic algorithms Mathematical model Time factors Vehicles Routing Evolutionary computation Constraint optimization System testing Systems engineering and theory

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Measuring flow as concept for detecting game fun in the Pac-Man game

Measuring flow as concept for detecting game fun in the Pac-...

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Congress on Evolutionary Computation

作者： Nicola Beume Holger Danielsiek Christian Eichhorn Boris Naujoks Mike Preuss Klaus Stiller Simon Wessing Chair of Algorithm Engineering Computational Intelligence Group Faculty Computer Science Dortmund University Germany

Popular games often have a high-quality graphic design but quite simple-minded non player characters (NPC). Recently, Computational Intelligence (CI) methods have been discovered as suitable methods to revive NPC, making games more interesting, challenging, and funny. We present a fairly large study of human players on the simple arcade game Pac-Man, controlling the ghosts behaviors by simple strategies, neural networks or evolutionary algorithms. The playerpsilas fun is of course a subjective experience, but we presume that it is related to the psychological flow concept. We deal with the question whether flow is a more reliable measure than asking human players directly for the fun experienced during the game. In order to detect flow, we introduce a measure based on the interaction time fraction between the human-controlled Pac-Man and the ghosts, and compare the outcome to the results of a fun measure suggested by Yannakakis and Hallam [1].

关键词： Games Computers Humans Fluid flow measurement Training Tiles Evolutionary computation

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A Parameterized Theory of PAC Learning

arXiv

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arXiv 2023年

作者： Brand, Cornelius Ganian, Robert Simonov, Kirill Algorithms and Complexity Group TU Wien Austria Chair for Algorithm Engineering Hasso Plattner Institute Germany

Probably Approximately Correct (i.e., PAC) learning is a core concept of sample complexity theory, and efficient PAC learnability is often seen as a natural counterpart to the class P in classical computational complexity. But while the nascent theory of parameterized complexity has allowed us to push beyond the P-NP "dichotomy" in classical computational complexity and identify the exact boundaries of tractability for numerous problems, there is no analogue in the domain of sample complexity that could push beyond efficient PAC learnability. As our core contribution, we fill this gap by developing a theory of parameterized PAC learning which allows us to shed new light on several recent PAC learning results that incorporated elements of parameterized complexity. Within the theory, we identify not one but two notions of fixed-parameter learnability that both form distinct counterparts to the class FPT—the core concept at the center of the parameterized complexity paradigm—and develop the machinery required to exclude fixed-parameter learnability. We then showcase the applications of this theory to identify refined boundaries of tractability for CNF and DNF learning as well as for a range of learning problems on graphs. © 2023, CC BY.

关键词： Computational complexity

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A Parallel Evolutionary System for Multi-objective Optimisation

A Parallel Evolutionary System for Multi-objective Optimisat...

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Congress on Evolutionary Computation

作者： Mohammad Hamdan Gunter Rudolph Nicola Hochstrate MACS Heriot Watt University Dubai UAE Chair of Algorithm Engineering Technische Universitat Dortmund Dortmund Germany

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Parallel evolutionary algorithms have been used for solving multiobjective optimization problems. The aim is to find or approximate the Pareto optimal set in a reasonable time. In this work, we present a new approach that divides the objective search-space into different partitions and assigns each processor its corresponding partition. Each processor will try to find the set of solutions for its partition only. The sub-Pareto fronts will be combined later and the parallelisation approach is based on a mutli-start approach by having independent algorithm on every processor with its own starting points. Experimental results on well known test cases showed that the proposed method outperformed several state-of-the-art evolutionary algorithms regarding convergence to the true Pareto front and gave very competitive results when considering the hypervolume metric. Also, superlinear speedup results were achieved for all test functions.

关键词： Optimization Sociology Statistics Linear programming Search problems Evolutionary computation Approximation algorithms

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Intelligent anti-grouping in real-time strategy games

Intelligent anti-grouping in real-time strategy games

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Nicola Beume Tobias Hein Boris Naujoks Nico Piatkowski Mike Preuss Simon Wessing Chair of Algorithm Engineering Computainal Interlligence Group Faculty of Computer Science Tecnnische Universität Dortmund Germany

Assembling suitable groups of fighting units to combat incoming enemy groups is a tactical necessity in real-time strategy (RTS) games. Furthermore it heavily influences future strategic decisions like unit building. Here, we demonstrate how to efficiently (offline) solve the problem of finding matches for the current enemy group(s) based on self-organizing maps (SOMs), powered by a simple evolutionary algorithm. The concept is implemented and thoroughly experimentally investigated in the RTS game Glest. We show that the offline learning is reliable and can be sped up considerably by employing a very simple substitute objective function instead of game simulations, making it a nearly universal, simple, and transparent technique.

关键词： Artificial intelligence Humans Assembly Self organizing feature maps Evolutionary computation Computational intelligence Computer science Filters Artificial neural networks Visualization

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To model or not to model: Controlling Pac-Man ghosts without incorporating global knowledge

To model or not to model: Controlling Pac-Man ghosts without...

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Congress on Evolutionary Computation

作者： Nicola Beume Tobias Hein Boris Naujoks Georg Neugebauer Nico Piatkowski Mike Preuss Raphael Stuer Andreas Thom Chair of Algorithm Engineering Computational Intelligence Group Faculty of Computer Science Technische Universität Dortmund Germany

The creation of interesting opponents for human players in computer games is an interesting and challenging task. In contrast to up-to-date computer games, e.g. real time strategy games, learning of non-player-character strategies for older games seems to be easier and not that time-consuming. This way, older games, like the famous arcade game Pac-Man, serve as a test bed for the creation of strategies that are fun to play against. The paper at hand uses computational intelligence methods to accomplish this challenge, namely evolutionary algorithms (EA) and artificial neural networks (ANN). The latter are trained on a model of the game whereas the EA learn good behavior by playing. The performance of these two approaches is compared on the original Pac-Man level as well as on other maps with different properties to test the ability of generalizing the learned strategies.

关键词： Games Artificial neural networks Training Humans Evolutionary computation Computational modeling Distance measurement

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