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"""
Fitness-Guided Crossover Operator.
Adapts LLEGO's fitness-guided crossover for text prompts.
Based on: Decision Tree Induction Through LLMs via Semantically-Aware Evolution (ICLR 2025)
"""
from typing import List, Callable, TYPE_CHECKING
import logging
from .base_operator import BaseCrossoverOperator
if TYPE_CHECKING:
from .models import PromptCandidate
logger = logging.getLogger(__name__)
class FitnessGuidedCrossover(BaseCrossoverOperator):
"""
Fitness-guided crossover for text prompts.
Combines high-performing parent prompts to generate offspring
that target specific fitness levels using LLM semantic understanding.
From LLEGO paper:
"Fitness-guided crossover exploits high-performing regions of the search space
by combining parent trees targeting a desired fitness level f* = f_max + α(f_max - f_min)"
Reference: https://github.com/nicolashuynh/LLEGO
"""
def __init__(self, alpha: float = 0.1):
"""
Initialize crossover operator.
Args:
alpha: Fitness extrapolation parameter.
Higher α = target higher fitness than parents.
Default 0.1 from LLEGO paper (target 10% above best parent).
"""
self.alpha = alpha
logger.debug(f"FitnessGuidedCrossover initialized with α={alpha}")
def __call__(
self,
parents: List["PromptCandidate"],
target_fitness: float,
llm: Callable[[str], str]
) -> str:
"""
Combine parent prompts targeting specific fitness.
Args:
parents: List of PromptCandidate objects (2+ parents)
target_fitness: Desired fitness for offspring
llm: Language model callable
Returns:
str: Offspring prompt
Raises:
ValueError: If fewer than 2 parents provided
"""
if len(parents) < 2:
raise ValueError("Crossover requires at least 2 parents")
# Sort parents by fitness (best first)
sorted_parents = sorted(parents, key=lambda p: p.fitness, reverse=True)
logger.debug(f"Crossover: {len(parents)} parents, target fitness={target_fitness:.3f}")
# Build crossover prompt and call LLM
crossover_prompt = self._build_prompt(sorted_parents, target_fitness)
new_prompt = llm(crossover_prompt)
return new_prompt
def _build_prompt(
self,
parents: List["PromptCandidate"],
target_fitness: float
) -> str:
"""
Build LLM prompt for crossover operation.
Args:
parents: Sorted list of parent candidates (best first)
target_fitness: Target fitness for offspring
Returns:
str: Prompt for LLM
"""
# Truncate parents to prevent safety filter issues
MAX_PARENT_LENGTH = 350
# Build parent descriptions (limit to top 2)
parent_descriptions = []
for i, parent in enumerate(parents[:2]):
truncated = parent.prompt[:MAX_PARENT_LENGTH]
if len(parent.prompt) > MAX_PARENT_LENGTH:
truncated += "..."
parent_descriptions.append(
f"P{i+1} (f={parent.fitness:.2f}): {truncated}\n"
)
prompt = f"""Combine these prompts into ONE improved version (target fitness: {target_fitness:.2f}).
{' '.join(parent_descriptions)}
Instructions:
1. Merge the best rules/principles from both parents
2. Organize logic clearly (e.g., "For X tasks: do Y", "If Z: then A")
3. Add structure to handle different cases systematically
4. Keep output format (Element: X, Description:, Reason:)
5. Max 600 chars
Output ONLY the combined prompt:"""
return prompt
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