Allowing evolutionary operators themselves to be subject to evolutionary change (meta-level modification) can make genetic programming more efficient.

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• Evolution often produces general-purpose problem-solving machines whose capacities cannot be inferred from the default invariant course of development.claim0.786
  A claim about the outcome of the MCA-enhanced process.
• By using a variational autoencoder-like architecture for genomic compression, evolution is freed from over-training and pushed to evolve general-purpose problem-solving machines.claim0.785
  Claim linking the indirect genotype-phenotype mapping to robustness and open-endedness.
• Evolution learns to generalize beyond default morphologies, producing problem-solving machines.claim0.781
  Argues that evolutionary learning goes beyond specific adaptations.
• Evolution makes problem-solving agents, not solutions; commitment to mutation and uncontrollable environments as ratchet for intelligenceclaim0.780
• The multiscale competency architecture (MCA) speeds evolutionary search by providing generalization, reliability, tractable search space, cryptic variation, and functional intermediates.claim0.777
  Main functional claim about MCA.
• Post-evolution performance is dominated by the task-solving agent's base capability, not by evolver identityclaim0.770
  Practical implication of Observation 2 in evolver-side analysis
• MCA enables generalization: many genotypes map to the same functional phenotype, increasing robustness and speeding exploration.claim0.770
  Subclaim of how MCA speeds evolution.
• Evolution actually searches not only genotype space but a more tractable space of behavior-shaping signals that exploit cellular intelligence.claim0.768
  Central thesis of the paper.