Co-evolution of self-replication and function in a digital primordial soup

2026-07-10

AI Summary

This study investigates whether self-replication and problem-solving functionality can co-evolve within a digital primordial soup devoid of predefined replication machinery. By introducing polynomial evaluation as a task that modulates interaction probabilities among a population of random 32-byte Z80 assembly programs—and relying solely on random mutation and program interactions—the work demonstrates, for the first time, the spontaneous co-emergence of self-replication and computational capabilities from entirely random initial conditions. The findings reveal that task-driven selection pressures shape replication architectures, metabolic constraints promote conditional termination behaviors, and spatial segregation fosters an emergent curriculum of increasing complexity. Experiments yield compact, robust replicators, markedly enhanced conditional termination, and the stable coexistence of self-replication with nontrivial mathematical computation.

Abstract

While traditional evolutionary algorithms hard-code reproduction, self-replication can emerge spontaneously within digital ``primordial soups''. This paper investigates the co-evolution of this emergent self-replication alongside problem-solving capabilities. We initialize a population of random 32-byte Z80 assembly programs, requiring self-replication to arise purely through random assembly-level mutations and pairwise program interactions. To link these behaviors, we introduce a task-based validation step: correctly evaluating a polynomial raises a program's interaction probability above a baseline rate. Our experiments yield four primary findings. First, self-replication and mathematical problem-solving successfully co-evolve from initial randomness. Second, the pressure to compute accelerates the emergence of compact, robust reproductive architectures that preserve memory for task execution. Third, applying metabolic constraints increases the likelihood that programs evolve conditional halting, terminating early during validation while bypassing the halt during interaction to execute block-copy replication. Finally, when programs are partitioned into spatial task niches, spontaneous self-replication generates an emergent learning curriculum, utilizing simple solutions as stepping stones toward complex polynomials. Altogether, these results demonstrate an interactive feedback loop: environmental task demands actively shape the physical architecture of self-replication, while spontaneous replication alters the evolutionary trajectory of functional problem-solving.

Problem

• self-replication
• co-evolution
• digital primordial soup
• problem-solving
• emergent computation

Innovation

• co-evolution
• self-replication
• digital primordial soup
• emergent computation
• task-driven evolution