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Bacterially inspired evolving system with an application to time series prediction

机译：受细菌启发的进化系统及其在时间序列预测中的应用

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This paper explores the synergies between evolutionary computation and synthetic biology, developing an in silico evolutionary system that is inspired by the behavior of bacterial populations living in continuously changing environments. This system creates a 3D environment seeded with a simulated population of bacteria that eat, reproduce, interact with each other and with the environment and eventually die. This provides a 3D framework implementing an evolutionary process. The subject of the evolution is each bacterium's internal process, defining its interactions with the environment. The evolutionary goal is the survival of the population under successive, continuously changing environmental conditions. The key advantage of this bacterial evolutionary system is its decentralized, asynchronous, parallel and self-adapting general-purpose evolutionary process. We describe this system and present the results of an application to the evolution of a bacterial population that learns how to predict the presence or absence of food in the environment by analyzing three input signals from the environment. The resulting populations successfully evolve by continuously improving their fitness under different environmental conditions, demonstrating their adaptability to a fluctuating medium.
机译：本文探索了进化计算与合成生物学之间的协同作用，开发了一种计算机进化系统，该系统受到生活在不断变化的环境中的细菌种群行为的启发。该系统创建了一个3D环境，其中植入了模拟的细菌种群，这些细菌会相互进食，繁殖，相互作用以及与环境相互作用，最终死亡。这提供了实现进化过程的3D框架。进化的主题是每种细菌的内部过程，定义了其与环境的相互作用。进化目标是在连续不断变化的环境条件下种群的生存。这种细菌进化系统的主要优势是其分散，异步，并行和自适应的通用进化过程。我们描述了该系统，并提出了一种应用于细菌种群进化的结果，该细菌种群通过分析来自环境的三个输入信号来学习如何预测环境中食物的存在与否。通过不断提高其在不同环境条件下的适应度，证明了它们对波动介质的适应性，从而成功地进化了种群。

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