Maze Generation Based on Difficulty using Genetic Algorithm with Gene Pool
Maze Generation Based on Difficulty using Genetic Algorithm with Gene Pool
复制标题
基于基因库的遗传算法基于难度的迷宫生成
DOI：
10.1109/isemantic50169.2020.9234216
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
A. Yunanto
中科院分区：
文献类型：
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作者：
Evan Kusuma Susanto;Rifqi Fachruddin;Muhammad Ihsan Diputra;D. Herumurti;A. Yunanto
游戏级别设计是开发一款赏心悦目的视频游戏的最重要的元素之一。此外，难度和动态水平的游戏可以让玩家更兴奋。提出了一种利用遗传算法生成视频游戏关卡的新方法。该方法被称为基因库集成学习。该方法在特征选择中实现，使该方法具有足够的通用性，可用于多种不同类型的游戏。本文使用一些训练数据来扫描好的模式，并将它们全部存储在基因库中。此外，使用遗传算法来寻找能够产生最佳结果的模式组合。基因库还记录了每个基因的质量，这样它就可以了解在多个水平上最常见的模式。为了测试，这项研究开发了一个定制游戏，与以前尝试的工作相比，它具有复杂的规则，很难用简单的2D数组来表示。研究结果表明，该方法可以一次生成多个复杂层次。总体而言，使用此方法生成的级别平均需要比数据集多3倍的步骤来求解。
Game level design is one of the most important element of developing an enjoyable video game. Besides, game with difficult and dynamic level can make players more exciting. This paper presents a new method of generating a video game level using a genetic algorithm. The proposed method is called gene pool integrates learning. This method implemented in feature selection so that this method is general enough to be used for multiple different types of games. This paper uses some training data to scan good patterns and store all of them in a gene pool. Furthermore, the genetic algorithm is used to find the combination of patterns that can produce the best result. The gene pool also records the quality of each gene so it can learn the pattern which most commonly found in multiple levels. For testing, this research develops a custom game with complicated rules that are hard to represent by a simple 2D array compared to the previously attempted work. The result of this research shows that the method can generate many complicated levels at once. Overall, levels generated using this method on average requires almost 3 times more steps to solve than the dataset.
在循环神经网络模型中利用混沌动力学解决二维迷宫问题
DOI：
--
发表时间：
2004
期刊：
Neural Computation Vol.16
影响因子：
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作者：
Yukio Takii;Kimiko Ikeda;Chihiro;Sato;Tutomu Sato;Hiroshi Konno;Yoshikazu Suemitsu and Shigetoshi Nara
通讯作者：
Yoshikazu Suemitsu and Shigetoshi Nara