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Evolve Python algorithms and programs using LLMs as mutation operators combined with MAP-Elites quality-diversity search, achieving competitive results on geometric optimization and algorithmic problems by iteratively mutating code informed by historical performance and lineage context.
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| name | gigaevo-llm-evolution |
| title | GigaEvo: Open Source Optimization Framework Powered By LLMs And Evolution |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2511.17592 |
| keywords | ["Evolutionary Algorithms","LLM Mutation","Algorithm Optimization","Program Synthesis","Quality-Diversity"] |
| description | Evolve Python algorithms and programs using LLMs as mutation operators combined with MAP-Elites quality-diversity search, achieving competitive results on geometric optimization and algorithmic problems by iteratively mutating code informed by historical performance and lineage context. |
Rather than optimizing neural network weights, this skill demonstrates how to evolve entire programs and algorithms using large language models as intelligent mutation operators. GigaEvo combines evolutionary computation (MAP-Elites quality-diversity algorithm) with LLM-based code generation, enabling discovery of novel algorithms for geometric optimization, combinatorial problems, and other domains where explicit algorithms may outperform learned models.
The core innovation is using LLMs not for inference, but as mutation operators that generate improved algorithm variants by analyzing parent code, performance metrics, and historical context about what changes succeeded.
GigaEvo implements an evolutionary framework where:
The system tracks bidirectional lineage (parent→offspring and offspring→parent) to enable context-aware code generation.
The evolutionary process cycles through population initialization, evaluation, and mutation stages.
1. Initialize Population and Behavior Space
Create initial population of random programs and define the behavior space dimensions.
def initialize_giga_evo(task_domain, population_size=100):
"""
Initialize the evolutionary population with random programs.
Sets up the behavior space for quality-diversity optimization.
"""
population = []
idx (population_size):
task_domain == :
program = generate_random_geometric_algorithm()
task_domain == :
program = generate_random_packing_algorithm()
individual = {
: ,
: program,
: ,
: ,
: {: [], : []},
: {}
}
population.append(individual)
behavior_space = {
: (, ),
: (, ),
: (, )
}
population, behavior_space
2. Execute and Evaluate Programs
Run each program on benchmark tasks and collect fitness, behavior, and metric data.
def evaluate_program(program_code, task_domain, task_instances):
"""
Execute program on benchmark tasks and collect metrics.
Handles crashes gracefully; invalid programs receive low fitness.
"""
try:
# Execute the program
executor = ProgramExecutor()
results = executor.run(program_code, task_instances, timeout=5.0)
# Compute fitness from results
fitness = compute_fitness_score(results, task_domain)
# Extract behavior coordinates
behavior = {
'fitness': fitness,
'efficiency': np.mean(results['runtimes']),
'complexity': measure_code_complexity(program_code)
}
metrics = {
'success_rate': results.get('success_rate', 0),
'mean_quality': np.mean(results.get('qualities', [0])),
'worst_quality': np.min(results.get('qualities', [0])),
'runtime': np.mean(results['runtimes'])
}
return {
'fitness': fitness,
'behavior': behavior,
'metrics': metrics,
'valid': True
}
except Exception as e:
return {
'fitness': -1000,
'behavior': None,
'metrics': {'error': str(e)},
'valid': False
}
3. Build Mutation Context from Lineage
Assemble rich context about the parent program, its performance, and historical mutations to guide mutation prompts.
def build_mutation_context(parent_individual, population_db):
"""
Construct detailed context for LLM mutation prompts.
Includes parent code, metrics, and successful historical changes.
"""
context = {
'parent_code': parent_individual['code'],
'parent_fitness': parent_individual['fitness'],
'parent_metrics': parent_individual['metrics'],
'successful_ancestors': [],
'descendant_performance': []
}
# Retrieve successful ancestors (parents with high fitness)
for ancestor_id in parent_individual['lineage']['ancestors']:
ancestor = population_db[ancestor_id]
if ancestor['fitness'] > parent_individual['fitness'] * 0.8:
context['successful_ancestors'].append({
'id': ancestor_id,
'code_snippet': ancestor['code'][:500],
'fitness': ancestor['fitness']
})
# Retrieve how descendants performed (learning from mutations)
for descendant_id in parent_individual['lineage']['descendants']:
descendant = population_db[descendant_id]
improvement = descendant['fitness'] - parent_individual['fitness']
context['descendant_performance'].append({
'improvement': improvement,
'type': 'positive' if improvement > 0 else 'negative'
})
return context
4. Generate Mutant via LLM
Use LLM as mutation operator, providing context about what worked in the past.
def mutate_via_llm(parent_individual, population_context, llm_agent):
"""
Generate offspring code using LLM mutation operator.
LLM receives parent code, metrics, and successful history.
"""
mutation_prompt = f"""
You are optimizing a Python algorithm for {parent_individual['domain']}.
Parent Algorithm:
{parent_individual['code']}
Parent Performance:
- Fitness Score: {parent_individual['fitness']:.2f}
- Mean Quality: {parent_individual['metrics'].get('mean_quality', 'unknown')}
- Runtime: {parent_individual['metrics'].get('runtime', 'unknown')}ms
Successful Historical Changes:
{format_successful_mutations(population_context['successful_ancestors'])}
Generate an improved version by:
1. Analyzing inefficiencies in the parent algorithm
2. Applying one concrete improvement (e.g., better search strategy, data structure change, pruning rule)
3. Keeping the core algorithm structure recognizable
Return ONLY the improved Python code, no explanations.
"""
mutant_code = llm_agent.generate_code(mutation_prompt)
# Validate syntax
try:
compile(mutant_code, '<string>', 'exec')
return {'code': mutant_code, 'valid': True}
except SyntaxError:
# Request repair if syntax errors
return {'code': mutant_code, 'valid': False, 'error': 'syntax'}
5. Maintain MAP-Elites Archive
Update the quality-diversity archive with new individuals, replacing dominated solutions.
def update_map_elites_archive(archive, individual, behavior_space):
"""
Update MAP-Elites archive with new individual.
Archives maintain diverse, high-performing solutions.
"""
# Discretize behavior into grid cell
cell = discretize_behavior(individual['behavior'], behavior_space)
if cell not in archive:
# Empty cell, add the individual
archive[cell] = individual
return True
else:
# Cell occupied, replace if individual is more fit
current_best = archive[cell]
if individual['fitness'] > current_best['fitness']:
archive[cell] = individual
return True
return False
6. Evolutionary Loop with Selection Pressure
Complete cycle: select parents, mutate, evaluate, update archive.
def evolutionary_loop(population, behavior_space, num_generations=50):
"""
Main evolutionary loop integrating selection, mutation, evaluation, and archiving.
Continues for specified generations or until convergence.
"""
archive = {} # MAP-Elites archive
history = []
for generation in range(num_generations):
# Select parent (quality-diversity biased)
parent = select_parent_from_population(population)
# Mutate via LLM
context = build_mutation_context(parent, population)
mutant_result = mutate_via_llm(parent, context, llm_agent)
if not mutant_result['valid']:
continue # Skip invalid mutations
# Evaluate mutant
eval_result = evaluate_program(mutant_result['code'], parent['domain'])
mutant = {
'code': mutant_result['code'],
'fitness': eval_result['fitness'],
'behavior': eval_result['behavior'],
'lineage': {
'ancestors': parent['lineage']['ancestors'] + [parent['id']],
'descendants': []
},
'metrics': eval_result['metrics']
}
# Update archive
added = update_map_elites_archive(archive, mutant, behavior_space)
# Track best fitness
best_fitness = max([ind['fitness'] for ind in archive.values()])
history.append(best_fitness)
population.append(mutant)
return archive, history
When to Use GigaEvo:
When NOT to Use:
Key Hyperparameters:
mutation_prompt_quality: Richer context → better mutations, but higher latencypopulation_size: Larger population enables better quality-diversity tradeoff (100-500 typical)num_generations: Usually 30-100 generations sufficient for convergencetask_timeout: Prevent infinite loops; 5-10 seconds typical for small instancesCost Optimization:
Integration with RL: Evolved algorithms can be evaluated in multi-agent benchmarks or used as baseline policies in environments. Combine with imitation learning to distill evolved algorithms into neural policies.
Research paper: https://arxiv.org/abs/2511.17592