Indirectly Encoded Sodarace for Artificial Life

The aim of this paper is to introduce a lightweight twodimensional domain for evolving diverse and interesting artificial creatures. The hope is that this domain will fill a need for such an easily-accessible option for researchers who wish to focus more on the evolutionary dynamics of artificial life scenarios than on building simulators and creature encodings. The proposed domain is inspired by Sodarace, a construction set for two-dimensional creatures made of masses and springs. However, unlike the original Sodarace, the indirectly encoded Sodarace (IESoR) system introduced in this paper allows evolution to discover a wide range of complex and regular ambulating creature morphologies by encoding them with compositional pattern producing networks (CPPNs), which are an established indirect encoding originally introduced for encoding large-scale neural networks. The result, demonstrated through a technique called novelty search with local competition (which are combined through multiobjective search), is that IESoR can discover a wide breadth of interesting and functional creatures, suggesting its potential utility for future experiments in artificial life.

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