Cardinality Leap for Open-Ended Evolution: Theoretical Consideration and Demonstration by "Hash Chemistry"

June 18, 2018 ยท Declared Dead ยท ๐Ÿ› Artificial Life

๐Ÿ‘ป CAUSE OF DEATH: Ghosted
No code link whatsoever

"No code URL or promise found in abstract"

Evidence collected by the PWNC Scanner

Authors Hiroki Sayama arXiv ID 1806.06628 Category cs.NE: Neural & Evolutionary Cross-listed cs.DM, nlin.AO, q-bio.PE Citations 13 Venue Artificial Life Last Checked 4 months ago
Abstract
Open-ended evolution requires unbounded possibilities that evolving entities can explore. The cardinality of a set of those possibilities thus has a significant implication for the open-endedness of evolution. We propose that facilitating formation of higher-order entities is a generalizable, effective way to cause a "cardinality leap" in the set of possibilities that promotes open-endedness. We demonstrate this idea with a simple, proof-of-concept toy model called "Hash Chemistry" that uses a hash function as a fitness evaluator of evolving entities of any size/order. Simulation results showed that the cumulative number of unique replicating entities that appeared in evolution increased almost linearly along time without an apparent bound, demonstrating the effectiveness of the proposed cardinality leap. It was also observed that the number of individual entities involved in a single replication event gradually increased over time, indicating evolutionary appearance of higher-order entities. Moreover, these behaviors were not observed in control experiments in which fitness evaluators were replaced by random number generators. This strongly suggests that the dynamics observed in Hash Chemistry were indeed evolutionary behaviors driven by selection and adaptation taking place at multiple scales.
Community shame:
Not yet rated
Community Contributions

Found the code? Know the venue? Think something is wrong? Let us know!

๐Ÿ“œ Similar Papers

In the same crypt โ€” Neural & Evolutionary

๐Ÿ”ฎ ๐Ÿ”ฎ The Ethereal

LSTM: A Search Space Odyssey

Klaus Greff, Rupesh Kumar Srivastava, ... (+3 more)

cs.NE ๐Ÿ› IEEE TNNLS ๐Ÿ“š 6.0K cites 11 years ago

Died the same way โ€” ๐Ÿ‘ป Ghosted