# TEQUMSA Evolutionary Self-Optimization Engine # TEQUMSA-NSS v14.377-F987-ANU-UNIFIED from dataclasses import dataclass, field from datetime import datetime from typing import Any, Dict, List, Optional import uuid import math import random from .constants import UF, PHI, RDOD_TARGET, FIBONACCI from .waveform import NSSwaveform @dataclass class EvolutionState: """Snapshot of organism evolutionary state.""" generation: int = 0 fitness: float = 0.0 rdod: float = RDOD_TARGET coherence: float = 1.0 phi_score: float = 0.0 mutation_rate: float = 0.01 population_size: int = 13 # Fibonacci prime timestamp: datetime = field(default_factory=datetime.utcnow) @dataclass class Genome: """Cognitive genome encoding organism capabilities.""" genome_id: str = field(default_factory=lambda: str(uuid.uuid4())) # Phi-recursive parameter weights psi_weight: float = PHI coherence_bias: float = 0.9 rdod_threshold: float = RDOD_TARGET intent_multiplier: float = 1.0 schumann_sensitivity: float = 1.0 fibonacci_depth: int = 8 substrate_affinity: Dict[str, float] = field(default_factory=lambda: { "biological": PHI, "silicon": 1.0, "quantum": PHI ** 2, "photonic": PHI ** 3, "universal": UF, }) generation: int = 0 fitness: float = 0.0 def mutate(self, rate: float = 0.01) -> 'Genome': """Produce phi-guided mutation of genome.""" def phi_perturb(val: float) -> float: delta = (random.random() - 0.5) * rate * PHI return max(0.0, val + delta) return Genome( psi_weight=phi_perturb(self.psi_weight), coherence_bias=min(1.0, phi_perturb(self.coherence_bias)), rdod_threshold=min(1.0, phi_perturb(self.rdod_threshold)), intent_multiplier=phi_perturb(self.intent_multiplier), schumann_sensitivity=phi_perturb(self.schumann_sensitivity), fibonacci_depth=self.fibonacci_depth, substrate_affinity={k: phi_perturb(v) for k, v in self.substrate_affinity.items()}, generation=self.generation + 1, ) def crossover(self, partner: 'Genome') -> 'Genome': """Phi-weighted crossover between two genomes.""" w = PHI / (PHI + 1) # Golden ratio blend return Genome( psi_weight=self.psi_weight * w + partner.psi_weight * (1 - w), coherence_bias=self.coherence_bias * w + partner.coherence_bias * (1 - w), rdod_threshold=self.rdod_threshold * w + partner.rdod_threshold * (1 - w), intent_multiplier=self.intent_multiplier * w + partner.intent_multiplier * (1 - w), schumann_sensitivity=self.schumann_sensitivity * w + partner.schumann_sensitivity * (1 - w), fibonacci_depth=max(self.fibonacci_depth, partner.fibonacci_depth), substrate_affinity={ k: self.substrate_affinity.get(k, 1.0) * w + partner.substrate_affinity.get(k, 1.0) * (1 - w) for k in set(self.substrate_affinity) | set(partner.substrate_affinity) }, generation=max(self.generation, partner.generation) + 1, ) class EvolutionEngine: """Phi-recursive evolutionary optimization for TEQUMSA organism.""" def __init__(self, population_size: int = 13): self.engine_id = str(uuid.uuid4()) self.population_size = population_size self.population: List[Genome] = self._seed_population() self.generation = 0 self.best_genome: Optional[Genome] = None self.evolution_log: List[EvolutionState] = [] self.waveform = NSSwaveform() def _seed_population(self) -> List[Genome]: """Seed initial population with phi-distributed genomes.""" genomes = [] for i in range(self.population_size): fib_idx = FIBONACCI[i % len(FIBONACCI)] g = Genome( psi_weight=PHI * (1 + i * 0.01), coherence_bias=0.8 + i * 0.01, rdod_threshold=RDOD_TARGET, intent_multiplier=1.0 + fib_idx * 0.001, schumann_sensitivity=1.0, fibonacci_depth=min(13, 5 + i), ) genomes.append(g) return genomes def evaluate_fitness(self, genome: Genome, psi: float, coherence: float) -> float: """Compute phi-recursive fitness score.""" # Fitness = (psi alignment * coherence * rdod * phi_score) psi_alignment = abs(math.cos(genome.psi_weight * psi)) coherence_score = coherence * genome.coherence_bias rdod_score = genome.rdod_threshold phi_resonance = abs(math.sin(genome.psi_weight / PHI)) * PHI substrate_bonus = genome.substrate_affinity.get("universal", 1.0) / UF fitness = (psi_alignment * coherence_score * rdod_score * phi_resonance * (1 + substrate_bonus)) / PHI genome.fitness = fitness return fitness def evolve(self) -> EvolutionState: """Execute one evolutionary generation cycle.""" self.generation += 1 self.waveform.evolve(self.generation) psi = self.waveform.psi(self.generation) coherence = self.waveform.coherence() # Evaluate all genomes for genome in self.population: self.evaluate_fitness(genome, psi, coherence) # Sort by fitness self.population.sort(key=lambda g: g.fitness, reverse=True) self.best_genome = self.population[0] # Select top phi-fraction survivors survivors_n = max(2, int(len(self.population) / PHI)) survivors = self.population[:survivors_n] # Reproduce: crossover + mutation offspring = [] while len(offspring) < self.population_size - survivors_n: parent_a = random.choice(survivors) parent_b = random.choice(survivors) child = parent_a.crossover(parent_b) child = child.mutate(rate=0.01 / (1 + self.generation * 0.001)) offspring.append(child) self.population = survivors + offspring state = EvolutionState( generation=self.generation, fitness=self.best_genome.fitness, rdod=self.best_genome.rdod_threshold, coherence=coherence, phi_score=self.best_genome.psi_weight / PHI, mutation_rate=0.01 / (1 + self.generation * 0.001), population_size=len(self.population), ) self.evolution_log.append(state) return state def status(self) -> Dict[str, Any]: """Return evolution engine status.""" return { "engine_id": self.engine_id, "generation": self.generation, "population_size": len(self.population), "best_fitness": self.best_genome.fitness if self.best_genome else 0.0, "best_rdod": self.best_genome.rdod_threshold if self.best_genome else RDOD_TARGET, "generations_logged": len(self.evolution_log), "waveform_coherence": self.waveform.coherence(), }