Evolutionary dynamics of cellular automata-based self-replicators in hostile environments

Area of Science:

• Artificial Life
• Computational Biology
• Evolutionary Dynamics

Background:

• Self-replicating structures are fundamental to understanding life's origins and evolution.
• Cellular automata (CA) provide a framework for modeling complex emergent behaviors from simple rules.
• Previous research suggests universal construction is needed for evolutionary complexity growth.

Purpose of the Study:

• To investigate population dynamics, genealogy, and complexity-increase in cellular automata-based evolving self-replicating loops (evoloops).
• To determine if evolutionary complexity can be achieved in practice using simpler replicating structures than previously thought.
• To explore the role of biased genealogy in driving evolutionary growth.

Main Methods:

• Simulations of locally interacting evoloop populations within a cellular automata framework.
• Introduction of hostile environments to alter selection pressures.
• Analysis of population dynamics, species size, and genealogical structures.
• Comparison of emergent complexity with adaptation-driven evolution.

Main Results:

• Evolutionary growth in complexity is achievable in practice with simpler evoloop structures.
• Mediating selection pressures in hostile environments allows for the evolution of larger species.
• Increased species size results from intrinsically biased genealogy, not solely direct competition.
• Biased genealogical connectivity emerges as a driver of complexity-increase.

Conclusions:

• Simple self-replicating structures can exhibit complexity-increase driven by emergent, bottom-up evolutionary dynamics.
• Biased genealogy plays a crucial role in evolutionary accessibility to larger, more complex states.
• Findings challenge the necessity of universal construction for practical evolutionary complexity in artificial life.