| name | Parent Selection Strategies |
| description | Evolutionary parent selection algorithms for choosing which generation to mutate next. Implements random, best, score-proportional, and novelty-aware selection. Triggers when selecting parents, managing exploration/exploitation tradeoffs, or configuring evolution strategy. |
| version | 1.0.0 |
| metadata | {"filePattern":["**/select*parent*","**/archive.jsonl","**/.hyperagents/config.json"],"bashPattern":["select.*parent","parent.*selection","exploration","exploitation"],"priority":70} |
Parent Selection Strategies
Parent selection is how HyperAgents balances exploration (trying new directions) with exploitation (refining what works). The choice of selection method significantly affects evolution dynamics.
Available Methods
random — Maximum Exploration
P(parent_i) = 1 / N for all valid parents
- Every valid parent has equal probability
- Maximum diversity in the search
- Good for early exploration when you don't know what works
- Risk: wastes compute on low-fitness branches
latest — Linear Chain
Always select the most recent valid generation
- Creates a simple chain: each generation builds on the last
- No branching, no backtracking
- Good for incremental refinement of a known approach
- Risk: can't escape local optima
best — Maximum Exploitation
Always select the highest-fitness generation
- Aggressively refines the best-known solution
- Fast convergence when near a good solution
- Risk: premature convergence, no exploration of alternatives
score_prop — Balanced (Recommended)
P(parent_i) = fitness_i / sum(all_fitness)
- Higher-scoring parents are more likely to be selected
- But any valid parent has a chance proportional to its score
- Natural balance of exploitation and exploration
- This is the default in HyperAgents
score_child_prop — Novelty-Aware
P(parent_i) = (fitness_i / (1 + children_i)) / Z
Where Z is the normalizing constant and children_i is the number of offspring already created from parent i.
- Favors high-fitness parents that haven't been explored much
- Encourages branching — explores different evolutionary paths
- Best for open-ended search where diversity matters
Selection Algorithm
def select_parent(archive, output_dir, domains, method):
candidates = {}
for genid in archive:
if not is_valid_parent(output_dir, genid):
continue
scores = [get_score(domain, output_dir, genid) for domain in domains]
if all(s is not None for s in scores):
candidates[genid] = mean(scores)
if method == "random":
return random.choice(list(candidates.keys()))
elif method == "latest":
return max(candidates.keys())
elif method == "best":
return max(candidates, key=candidates.get)
elif method == "score_prop":
weights = list(candidates.values())
return random.choices(list(candidates.keys()), weights=weights, k=1)[0]
elif method == "score_child_prop":
child_counts = count_children(archive, output_dir)
weights = [
score / (1 + child_counts.get(gid, 0))
for gid, score in candidates.items()
]
return random.choices(list(candidates.keys()), weights=weights, k=1)[0]
Validity Check
A generation is a valid parent if:
valid_parent: true in its metadata- It has non-null fitness scores for all active domains
- It was not marked as failed by the meta-agent
When to Use Each Method
| Scenario | Recommended Method |
|---|
| Starting evolution, unknown domain | random |
| Refining a known-good approach | best |
| General purpose, most cases | score_prop |
| Open-ended exploration, diversity matters | score_child_prop |
| Ablation study, control condition | latest |
| Many generations complete, seeking novelty | score_child_prop |
Diagnostics
Signs of poor parent selection:
- All offspring from one parent: Switch to
score_child_prop
- No improvement for 5+ generations: Switch to
random temporarily
- Fitness oscillating: Switch to
best to stabilize
- Archive diversity too low: Switch to
random or score_child_prop
