Introduce Differential Evolution (DE) as a generation-end refinement step for optimising numeric constants in GP individuals, instead of applying DE during offspring creation.

This yields a clean separation between:

GP → structural search
DE → numerical optimisation

DE acts as a population-level local search phase applied between generations.

Refinement policy

• Apply DE only to a subset of the population:

  • top 10-20% (elite) individuals;
  • optionally + small random subset (diversity).

• Optional scheduling:

  • every generation (cheap DE only);
  • every N generations (e.g. 2–5).

• Skip:

  • individuals without tunable parameters;
  • recently refined individuals;
  • oversized individuals (too many parameters).

Key design point

Lamarckian update: write optimised constants back into the genotype (offspring inherit improved parameter).