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Genetic Programming Needs Better Benchmarks
"... Genetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its benchmark problems are popular purely through historical contingency, and they can be criticized as too easy or as providing misleading information concerning real-world performance, but they persist largely ..."
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Genetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its benchmark problems are popular purely through historical contingency, and they can be criticized as too easy or as providing misleading information concerning real-world performance, but they persist largely because of inertia and the lack of good alternatives. Even where the problems themselves are impeccable, comparisons between studies are made more difficult by the lack of standardization. We argue that the definition of standard benchmarks is an essential step in the maturation of the field. We make several contributions towards this goal. We motivate the development of a benchmark suite and define its goals; we survey existing practice; we enumerate many candidate benchmarks; we report progress on reference implementations; and we set out a concrete plan for gathering feedback from the GP community that would, if adopted, lead to a standard set of benchmarks.
Searching for the Optimal Racing Line Using Genetic Algorithms
"... Abstract — Finding the racing line to follow on the track is at the root of the development of any controller in racing games. In commercial games this issue is usually addressed by using human-designed racing lines provided by domain experts and represents a rather time consuming process. In this p ..."
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Abstract — Finding the racing line to follow on the track is at the root of the development of any controller in racing games. In commercial games this issue is usually addressed by using human-designed racing lines provided by domain experts and represents a rather time consuming process. In this paper we introduce a novel approach to compute the racing line without any human intervention. In the proposed approach, the track is decomposed into several segments where a genetic algorithm is applied to search for the best trade-off between the minimization of two conflicting objectives: the length and the curvature of the racing line. The fitness of the candidate solutions is computed through a simulation performed with The Open Racing Car Simulator (TORCS), an open source simulator used as testbed in this work. Finally, to test our approach we carried out an experimental analysis that involved 11 tracks provided with the TORCS distribution. In addition, we compared the performance of our approach to the one achieved by a related approach, previously introduced in the literature, and to the performance of the fastest controller available for TORCS. Our results are very promising and show that the presented approach is able to reach the best performance in almost all the tracks considered. I.
Overtaking Opponents with Blocking Strategies Using Fuzzy Logic
"... Abstract — In car racing, blocking refers to maneuvers that can prevent, disturb or possibly block an overtaking action by an incoming car. In this paper, we present an advanced overtaking behavior that is able to deal with opponents implementing advanced blocking strategies. The behavior we develop ..."
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Abstract — In car racing, blocking refers to maneuvers that can prevent, disturb or possibly block an overtaking action by an incoming car. In this paper, we present an advanced overtaking behavior that is able to deal with opponents implementing advanced blocking strategies. The behavior we developed has been integrated in an existing fuzzy-based architecture for driving simulated cars and tested using The Open Car Racing Simulator (TORCS). We compared a driver implementing our overtaking strategy against six of the bots available in the TORCS distribution and simplix, a state-of-the-art bot which won the 2009 TORCS Endurance World Championship. The comparison was carried out against opponents implementing three blocking strategies of increasing difficulty. The results we present show that our strategy can overtake the opponent car in all the considered scenarios. In contrast, all the other bots can complete the overtaking maneuvers in only less than 40% of the cases. Our strategy is slightly more risky than others and may result in limited rear and lateral damage. Other more cautious drivers receive almost no damage, however they can overtake only around 30 % of the cases. I.
to the original GECCO publication and an earlier unnumbered corrective version. Genetic Programming Needs Better Benchmarks
"... Genetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its benchmark problems are popular purely through historical contingency, and they can be criticized as too easy or as providing misleading information concerning real-world performance, but they persist largely ..."
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Genetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its benchmark problems are popular purely through historical contingency, and they can be criticized as too easy or as providing misleading information concerning real-world performance, but they persist largely because of inertia and the lack of good alternatives. Even where the problems themselves are impeccable, comparisons between studies are made more difficult by the lack of standardization. We argue that the definition of standard benchmarks is an essential step in the maturation of the field. We make several contributions towards this goal. We motivate the development of a benchmark suite and define its goals; we survey existing practice; we enumerate many candidate benchmarks; we report progress on reference implementations; and we set out a concrete plan for gathering feedback from the GP community that would, if adopted, lead to a standard set of benchmarks.
An Evolutionary Tuned Driving System for Virtual Car Racing Games: The AUTOPIA
"... This work presents a driving system designed for virtual racing situations. It is based on a complete modular architecture capable of automatically driving a car along a track with or without opponents. The architecture is composed of intuitive modules, with each one being responsible for a basic as ..."
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This work presents a driving system designed for virtual racing situations. It is based on a complete modular architecture capable of automatically driving a car along a track with or without opponents. The architecture is composed of intuitive modules, with each one being responsible for a basic aspect of car driving. Moreover, this modularity of the architecture will allow us to replace or add modules in the future as a way to enhance particular features of particular situations. In the present work, some of the modules are implemented by means of hand-designed driving heuristics, whereas modules responsible for adapting the speed and direction of the vehicle to the track’s shape, both critical aspects of driving a vehicle, are optimized by means of a genetic algorithm that evaluates the performance of the controller in four different tracks to obtain the best controller in a large number of situations; the algorithm also penalizes controllers that go out of the track, lose control, or get damaged. The evaluation of the performance is done in two ways. First, in runs with and without adversaries over several tracks. And second, the architecture was submitted as a participant to the 2010 Simulated Car Racing Competition, which in end won laurels. C © 2012 Wiley Periodicals, Inc. 1.
Evolving Controllers for the Robot Auto Racing Simulator
"... Abstract We use evolutionary computation techniques to create real-time reactive controllers for a race-car simulation game: RARS (Robot Auto Racing Simula-tor). Using genetic programming to evolve driver con-trollers, we create highly generalized game-playing agents, able to outperform most human-c ..."
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Abstract We use evolutionary computation techniques to create real-time reactive controllers for a race-car simulation game: RARS (Robot Auto Racing Simula-tor). Using genetic programming to evolve driver con-trollers, we create highly generalized game-playing agents, able to outperform most human-crafted controllers and all machine-designed ones on a variety of game tracks. 1
