Initialisation & Evaluation

At task launch, we random-seed a population P₀ of size N (default = 64) by:

• Model heterogeneity – random choice among the registryʼs base checkpoints.

• Prompt genotype – shuffled system + user templates.

• Hyper-gene vector – temperature, top-p, context-window, RAG-source toggles.

• Structural genes – optional tool-use abilities enabled/disabled.

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Evaluator agents are responsible for benchmarking new agents on:

• Accuracy, coherence, novelty

• Task-specific performance

• Resource efficiency

• Goal generalization

These evaluators evolve themselves, closing the loop.

Evaluators therefore act as an adaptive fitness landscape, co-evolving with the agent population exactly as GA literature prescribes for dynamic optimisation problems.