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Computational discovery of instructionless self-replicating structures in cellular automata
1IBM Annapolis Lab, Annapolis, MD, USA. zpan@us.ibm.com
Artificial Life
|October 28, 2009
Summary
Genetic programming automatically creates self-replicating cellular automata rules, even for large structures. Discovered replicators exhibit novel, fast, fission-like reproduction without clear parent-child distinctions.
Area of Science:
- Artificial Life
- Computational Biology
- Evolutionary Computation
Background:
- Cellular automata (CA) models are foundational for studying self-replication.
- Previous research relied on manually designed replicators with identifiable structures.
Purpose of the Study:
- To investigate the efficacy of genetic programming (GP) in automatically generating CA replication rules.
- To explore novel self-replication mechanisms beyond human design.
Main Methods:
- Utilized genetic programming to evolve CA rules from arbitrary initial configurations.
- Analyzed the emergent replication strategies and properties of the generated structures.
Main Results:
- GP successfully generated self-replicating rules for CA structures of 50+ components.
- Evolved replicators displayed unique mechanisms: no distinct instruction sequence, translation/rotation during reproduction, and parallel fission-like division.
- Replication was rapid, obscuring parent-offspring relationships.
- Some replicators concurrently deposited secondary structures.
Conclusions:
- Genetic programming is a potent tool for automated discovery of self-replication in cellular automata.
- The evolved mechanisms offer insights into efficient and novel forms of self-replication.
- GP's applicability extends beyond cellular spaces to other computational and biological systems.
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