The conventional model of evolution centres on elimination: the unfit die, the fit survive, and natural selection prunes the population with ruthless efficiency. This model is powerful but incomplete.
In evolving software, a different dynamic is possible: and often more accurately descriptive of how adaptive systems actually behave. Variation persists. What changes is influence.
When a shared goal is completed: a board filled, a metric optimised, a target reached: the system does not purge. The instance with the highest success metric gains a specific privilege: it suggests the next objective for all variations to attempt.
This is a fundamental distinction. In this model, evolution proceeds not through death but through shifting influence. The population retains its full range of variation, available for future use under changed conditions.
If a variation ceases to exist, it is not because another instance destroyed it. It is because the environment: memory, compute, storage, network capacity: reached its limit. The constraint layer governs population, not the competitive layer.
This reframes evolutionary thinking in a significant way. Adaptive systems may accumulate diversity indefinitely, with influence shifting dynamically rather than terminating structural variation. The result is a population that is simultaneously convergent (in direction) and divergent (in capability).
The implications are profound: a system that preserves its unsuccessful variations maintains a reservoir of exploratory potential. If conditions change: if a new constraint emerges, a new feedback signal appears, a new resource becomes available: previously "unsuccessful" variations may become the most adapted.
When the most successful instance suggests the next goal, a higher-order dynamic emerges: collective goal-setting without centralised planning. No instance votes. No instance negotiates. Performance determines influence, and influence determines direction.
Over multiple cycles, this produces a trajectory: a sequence of goals that reflects the system's cumulative learning. The direction is not predetermined. It emerges from iterative performance under constraint.
Once the board is full of red: every cell claimed, every hit guaranteed to find an empty square: the program with the most wins gets to suggest the next goal for all the variations to attempt. No instances are deleted. If one ever ceases, it is the external restriction of environmental capacity that caused it, not internal competition.
Watch how direction emerges from performance. Watch how diversity persists alongside convergence. Watch how the system learns without centralised control.
Success informs direction. Failure still contributes diversity. Environment governs scale.