Multiple Worlds Model of Evolution for demographic appropriate radio playlists

J. A. Brown, D. Ashlock
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引用次数: 1

Abstract

This study presents an application of the Multiple Worlds Model of Evolution. The goal is to model radio stations in a given market. The model captures listener demographics and maximizes listeners, while securing advertising revenue. Listener preferences for different types of content are set as positive (like) and negative (dislike) integers, allowing surveys of the demographic to act as the model parameters directly. Fitness evaluation is performed with a modeled hour of radio playtime where stations can select between a set of content types and advertisements. Advertisements provide fitness in the form of advertising revenues; however, listeners will only stay on a station which provides content they enjoy. The Multiple Worlds Model is a form of multiple population evolutionary algorithm. It evaluates fitness based on the actions of one member from each population, and has no genetic transfer of information between populations. Each population can thus specialize. In the current study, such specialization is a self-organization of focused (e.g. rock or country) stations via adaption to listener preferences. The model is examined using different numbers of independent populations with even splits among demographic types. The evolved stations show differences in playlists where the profiles differ in their enjoyments and convergence between stations where the listener profiles are similar.
适合人口统计的广播播放列表的多重世界进化模型
本研究提出了多世界进化模型的一个应用。目标是在给定的市场中建立广播电台的模型。该模式捕捉听众的人口统计数据,最大化听众,同时确保广告收入。听众对不同类型内容的偏好被设置为正(喜欢)和负(不喜欢)整数,允许人口统计调查直接充当模型参数。健康评估是通过一个小时的广播播放时间来完成的,电台可以在一组内容类型和广告之间进行选择。广告以广告收入的形式提供健身;然而,听众只会留在提供他们喜欢的内容的电台。多世界模型是多种群进化算法的一种形式。它根据每个群体中一个成员的行为来评估适应度,并且在群体之间没有遗传信息的传递。因此,每个种群都可以专业化。在目前的研究中,这种专业化是通过适应听众的喜好,将重点(例如摇滚或乡村)电台自组织起来。该模型使用不同数量的独立人口进行检验,人口统计类型之间的分裂是均匀的。进化后的电台在播放列表中表现出不同,听众的喜好不同,而在听众的喜好相似的电台之间表现出趋同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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