Island Ga

Island Genetic Algorithm (IslandGA) Skill

Reference: Classic distributed evolutionary computation model, widely used for parallel evolutionary search. Independent sub-populations (islands) evolve in parallel with periodic migration.

1. Method Essence

IslandGA runs a distributed evolutionary algorithm in which the population is divided into several independent islands (sub-populations). Each island evolves on its own — with independent selection, crossover, and mutation — and only exchanges individuals through periodic migration events. This decoupled structure preserves genetic diversity (each island drifts toward different optima) and enables parallel execution of evolution workloads.

Core mechanisms:

2. Recommended Parameters

See params.yaml in this directory for the recommended parameter configuration.

Total population = num_islands × island_population_size.

What Happens During Evolution

1. Split the population into num_islands islands, each initialized independently
2. Each island runs its own GA loop (selection → crossover → mutation → evaluation)
3. Every migration_interval generations, a migration event occurs:
   • Select emigrants per the migration_strategy and migration_rate
   • Send them along the migration_topology to neighboring islands
   • Incoming migrants are inserted into the target island population
4. Islands evolve in parallel (parallel_islands) or sequentially
5. Final best individual across all islands is the solution

Common Pitfalls

• Too few islands or uniform islands → no diversity benefit; add more islands
• Migration too frequent or too high rate → premature homogenization/collapse
• Migration too rare → islands cannot share good solutions; slow convergence
• No topology diversity → ring is a safe default; fully-connected spreads fastest
• Failure to enable parallel_islands on large runs → unnecessarily slow

4. Acceptance Criteria

• Multiple islands evolve independently with observable divergence
• Periodic migration events occur at the configured interval
• Migrants cross islands and improve the receiving population
• Diversity is maintained across islands (not a single converged population)
• Final best solution improves over a single-population baseline