Show & Tell 36.5 — EvolutionaryOrchestrator: Generation Lifecycle & Convergence

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EvolutionaryOrchestrator — Algorithm Deep-Dive

Generation Lifecycle

RUN_EVOLUTION(config, fitness_fn):
  population ← initialize_population(config.population_size)
  history ← []
  
  for gen in 1..config.max_generations:
    // 1. Evaluate fitness
    for ind in population:
      ind.fitness ← fitness_fn(ind)
    
    // 2. Multi-objective ranking (if enabled)
    if config.pareto:
      fronts ← fast_non_dominated_sort(population)
      assign_crowding_distance(fronts)
    
    // 3. Novelty computation (if enabled)
    if config.novelty:
      for ind in population:
        ind.novelty ← compute_novelty(ind, archive)
        ind.blended_fitness ← blend(ind.fitness, ind.novelty, config.novelty.weight)
    
    // 4. Speciation
    if config.speciation:
      species ← speciate(population, config.speciation)
    
    // 5. Selection & reproduction
    new_population ← []
    for each species:
      parents ← select(species.members, config.selection)
      for pair in pairs(parents):
        child_a, child_b ← crossover(pair, config.crossover)
        child_a ← mutate(child_a, config.mutation, gen)
        child_b ← mutate(child_b, config.mutation, gen)
        new_population.extend([child_a, child_b])
    
    // 6. Elitism
    population ← apply_elitism(population, new_population, config.selection)
    
    // 7. Island migration (if enabled)
    if config.island and gen % config.island.migration_interval == 0:
      migrate(islands, config.island)
    
    // 8. Adaptive parameter control
    config ← adapt_parameters(config, history)
    
    // 9. Convergence check
    result ← GenerationResult(gen, best, mean, diversity, ...)
    history.append(result)
    if check_convergence(history, config.convergence):
      break
  
  return EvolutionResult(best, pareto_front, history, ...)

Convergence Detection

Three criteria (any triggers convergence):

1. Stagnation: best fitness unchanged for stagnation_window generations
2. Diversity floor: genotypic diversity drops below diversity_floor
3. Target reached: fitness exceeds target_fitness (if specified)

Adaptive Parameter Control (Eiben & Smith 2015)

1/5 Success Rule (Rechenberg 1973):

if success_rate > 1/5:
  σ ← σ / c  // decrease step size (exploit)
elif success_rate < 1/5:
  σ ← σ * c  // increase step size (explore)
// c = 0.817 (Schwefel)

Self-Adaptive Mutation (Bäck & Schwefel 1993):

• Encode σ in the genome itself
• σ evolves alongside solution parameters
• More robust than fixed schedules

Cross-Phase Integration

• Phase 35 (Quantum): Evolve quantum circuit architectures — genomes encode gate sequences
• Phase 14 (Code Synthesis): Evolve program ASTs via genetic programming
• Phase 33 (Continual Learning): Evolutionary curriculum scheduling

Related: #743