Bacterially inspired evolving system with an application to time series prediction

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.

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► We build an asynchronous, decentralized, general-purpose and continuous evolutionary system.
► It is inspired by bacterial populations and it evolves many kinds of intelligent systems.
► The population is subject to constant improvement and adaptation to its habitat.
► The population successfully evolves under different environmental conditions.
► Constant evolution and 3D simulation equip the system to deal with real-world problems.