Abstract
This paper explores the evolution of three-dimensional objects with a simple
generative encoding, known as the Superformula. Evolving three-dimensional
objects has long been of interest in a wide array of disciplines, from
engineering (e.g., robotics) to biology (e.g., studying morphological
evolution). While many representations have been presented, ranging from direct
encodings to complex graphs and grammars, the vast majority have possessed
complex underlying encodings, which were necessary to produce varied
morphologies. Here we explore the target-based evolution of Superformula, which
is simply encoded as a vector of reals and show that is possible to generate
very closely matching designs of a number of complex three-dimensional objects.
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