Evolving a TORCS Modular Fuzzy Driver Using Genetic Algorithms

Salem, Mohammed; Mora, Antonio M.; Merelo, J. J.; García‐Sánchez, Pablo

doi:10.1007/978-3-319-77538-8_24

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…The common approach is to define a list of game state features and evolve a vector of associated weights, so that they closely approximate the probability of winning the game from the given state. This type of parameter-learning evolution has been applied to numerous games, including Chess [11], Checkers [12], TORCS [13], Hearthstone [14].…”

Section: Background a Evolving Evaluation Functionsmentioning

confidence: 99%

Evolving Evaluation Functions for Collectible Card Game AI

Miernik¹,

Kowalski²

2021

Preprint

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In this work, we presented a study regarding two important aspects of evolving feature-based game evaluation functions: the choice of genome representation and the choice of opponent used to test the model.We compared three representations. One simpler and more limited, based on a vector of weights that are used in a linear combination of predefined game features. And two more complex, based on binary and n-ary trees.On top of this test, we also investigated the influence of fitness defined as a simulation-based function that: plays against a fixed weak opponent, plays against a fixed strong opponent, and plays against the best individual from the previous population.For a testbed, we have chosen a recently popular domain of digital collectible card games. We encoded our experiments in a programming game, Legends of Code and Magic, used in Strategy Card Game AI Competition. However, as the problems stated are of general nature we are convinced that our observations are applicable in the other domains as well.

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Section: Background a Evolving Evaluation Functionsmentioning

confidence: 99%

Evolving Evaluation Functions for Collectible Card Game AI

Miernik¹,

Kowalski²

2021

Preprint

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“…On the other, we need to execute one or more simulations to establish the performance of one configuration. These time-consuming problems make the manual tuning of parameters impractical [9]. Engineers often use metaheuristics not only to tune or adjust the parameters of fuzzy controllers but also to define the entire fuzzy controller structure.…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms include both evolutionary algorithms (EAs) [11] and swarm intelligence (SI) [12]. Most researchers follow a fuzzyevolutionary approach to optimize the parameters of fuzzy controllers [9,13].…”

Section: Introductionmentioning

confidence: 99%

Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

et al. 2023

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Designing a controller is typically an iterative process during which engineers must assess the performance of a design through time-consuming simulations; this becomes even more burdensome when using a population-based metaheuristic that evaluates every member of the population. Distributed algorithms can mitigate this issue, but these come with their own challenges. This is why, in this work, we propose a distributed and asynchronous bio-inspired algorithm to execute the simulations in parallel, using a multi-population multi-algorithmic approach. Following a cloud-native pattern, isolated populations interact asynchronously using a distributed message queue, which avoids idle cycles when waiting for other nodes to synchronize. The proposed algorithm can mix different metaheuristics, one for each population, first because it is possible and second because it can help keep total diversity high. To validate the speedup benefit of our proposal, we optimize the membership functions of a fuzzy controller for the trajectory tracking of a mobile autonomous robot using distributed versions of genetic algorithms, particle swarm optimization, and a mixed-metaheuristic configuration. We compare sequential versus distributed implementations and demonstrate the benefits of mixing the populations with distinct metaheuristics. We also propose a simple migration strategy that delivers satisfactory results. Moreover, we compare homogeneous and heterogenous configurations for the populations’ parameters. The results show that even when we use random heterogeneous parameter configuration in the distributed populations, we obtain an error similar to that in other work while significantly reducing the execution time.

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“…Moreover, the option of making a manual selection of these parameters is difficult because the search space is of considerable size and requires the validation of establishing the controller's performance by running time-consuming simulations. Evolutionary Algorithms (EAs) and other population-based metaheuristics are often employed in tuning Fuzzy Inference Systems (FISs) [18,19].…”

Section: Introductionmentioning

confidence: 99%

Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

et al. 2022

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In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human users. Moreover, a new technique that uses three different paths to validate the performance of each candidate configuration is presented. We extend on our previous work by adding two more membership functions to the previous fuzzy model, intending to have a finer-grained adjustment. We tuned the controller using several well-known metaheuristic methods, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Harmony Search (HS), and the recent Aquila Optimizer (AO) and Arithmetic Optimization Algorithms. Experiments validate that, compared to published results, the proposed fuzzy controllers have better RMSE-measured performance. Nevertheless, experiments also highlight problems with the common practice of evaluating the performance of fuzzy controllers with a single problem case and performance metric, resulting in controllers that tend to be overtrained.

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Evolving a TORCS Modular Fuzzy Driver Using Genetic Algorithms

Cited by 10 publications

References 18 publications

Evolving Evaluation Functions for Collectible Card Game AI

Evolving Evaluation Functions for Collectible Card Game AI

Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

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