GUCA: эволюция на графах («Живые графы» 2.0)

1. Введение. От вопросов об эволюции к Artificial Life 1.1 Вопросы эволюции Где проходит граница между случайностью и замыслом? Почему в наших ДНК куча мусора? Можно ли воспроизвести эволюцию в...

Introducing LACE – A New Kind of Cellular Automata | Nova Spivack

This article is about a new kind of simple computational rule ("LACE rules" running on LACE, the Link Automata Computing Engine platform) which, when applied locally on a grid of cells, demonstrates fascinating emergent "artificial life" behavior. For readers familiar with the Game of Life (GOL), this is a next-level class of cellular automata that

Stanisław Lem's Prescient Vision of Artificial Life

As with the best science fiction, Lem’s novel “The Invincible” has as much to teach us about our present situations as any futures we may face.

Stanisław Lem's Prescient Vision of Artificial Life

As with the best science fiction, Lem’s novel “The Invincible” has as much to teach us about our present situations as any futures we may face.

xCoAx Proceedings

Evolving Visual Routines

Abstract. Traditional machine vision assumes that the vision system recovers a complete, labeled description of the world [10]. Recently, several researchers have criticized this model and proposed an alternative model that considers perception as a distributed collection of task-specific, context-driven visual routines [1, 12]. Some of these researchers have argued that in natural living systems these visual routines are the product of natural selection [11]. So far, researchers have hand-coded task-specific visual routines for actual implementations (e.g., [3]). In this article we propose an alternative approach in which visual routines for simple tasks are created using an artificial evolution approach. We present results from a series of runs on actual camera images, in which simple routines were evolved using genetic programming techniques [7]. The results obtained are promising: The evolved routines are able to process correctly up to 93% of the test images, which is better than any algorithm we were able to write by hand.