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Advanced Structural Mutations and Co-evolution System

[!NOTE] 最新の実装状況は 機能実装ステータス (Remaining Functionality) を参照してください。

Date: 2026-03-11
Author: Masahiro Aoki
Copyright: 2026 Moonlight Technologies Inc.

Overview

This document describes the implementation of Plan F Phase 5-6 features: - Advanced Structural Mutations: Dynamic network architecture evolution - Co-evolution System: Multi-population evolutionary interactions

These features represent the most advanced capabilities of the L5 Self-Evolution system, enabling sophisticated architecture search and multi-agent evolutionary dynamics.

1. Advanced Structural Mutations

1.1 Overview

The Advanced Structural Mutations system (evospikenet/advanced_mutations.py) implements sophisticated mutations that modify the network architecture itself, going beyond simple parameter mutations.

1.2 Key Features

1.2.1 Layer Structure Changes

Add Layer: - Intelligently inserts new layers between existing ones - Interpolates layer size from neighbors - Automatically updates connection matrix - Respects min/max layer constraints (2-10 layers)

Remove Layer: - Removes layers while maintaining connectivity - Reconnects neighboring layers - Preserves network functionality - Minimum 2 layers maintained

Layer Size Constraints: - Min layer size: 32 neurons - Max layer size: 2048 neurons - Size step: 32 neurons (for alignment)

1.2.2 Connection Topology Optimization

Skip Connections: - Adds connections between non-adjacent layers (≥2 layers apart) - Enables gradient flow and residual learning - Removes redundant skip connections

Feedback Connections: - Adds recurrent connections from later to earlier layers - Enables temporal processing and memory - Tracks recurrent connections separately

Sparsification: - Removes 20-30% of non-essential connections - Reduces computational cost - Improves generalization

Densification: - Adds 10-20% more connections - Increases network capacity - Improves expressiveness

1.2.3 Activation Function Evolution

Available Activation Functions: - ReLU (default) - Leaky ReLU - ELU - Sigmoid - Tanh - GELU - Swish - Mish

Evolution Process: - Randomly selects layers for activation change - Chooses from available activation functions - Tracks activation function changes

1.2.4 Module-Level Mutations

Add Module: - Creates new functional modules (PFC, Memory, Attention, etc.) - Initializes with simple feedforward topology - Assigns energy allocation (default: 0.1) - Generates appropriate genes

Remove Module: - Removes modules with low usage (< 10%) - Maintains minimum 2 modules - Redistributes energy allocation

Available Module Types: - PFC (Prefrontal Cortex) - Memory - Attention - Motor - Vision - Language - Sensory

1.3 Configuration

@dataclass
class StructuralMutationConfig:
    # Layer mutations
    add_layer_rate: float = 0.05
    remove_layer_rate: float = 0.03
    max_layers: int = 10
    min_layers: int = 2

    # Module mutations
    add_module_rate: float = 0.02
    remove_module_rate: float = 0.01
    min_module_usage: float = 0.1

    # Connection mutations
    sparsify_rate: float = 0.1
    densify_rate: float = 0.1
    add_skip_connection_rate: float = 0.15
    remove_skip_connection_rate: float = 0.05
    add_feedback_connection_rate: float = 0.08

    # Activation mutations
    change_activation_rate: float = 0.12

    # Layer size constraints
    min_layer_size: int = 32
    max_layer_size: int = 2048
    layer_size_step: int = 32

1.4 Usage Example

# Advanced structural mutation with AdvancedMutationEngine (v2.2.0+)
from evospikenet.genome_pool import GenomePool
from evospikenet.genome import create_initial_genome
from evospikenet.evolution_engine import MutationEngine
from evospikenet.advanced_mutations import AdvancedMutationEngine, AdaptiveMutationConfig
from evospikenet.genome_to_brain import GenomeToBrainConverter
import asyncio

# Build the mutation pipeline: basic mutations + advanced structural mutations
advanced = AdvancedMutationEngine(AdaptiveMutationConfig(base_rate=0.1))
engine = MutationEngine(base_mutation_rate=0.05, advanced_engine=advanced)

# Initialize genome pool
pool = GenomePool(population_size=10)
pool.initialize_population()

# Mutate via the integrated pipeline
original = pool.genomes[0]
mutated = engine.mutate_genome(original)

converter = GenomeToBrainConverter()
network = converter.genome_to_network(mutated)
print('Mutated genome layers:', getattr(network, 'num_layers', 'N/A'))
print('Mutation log entries:', len(mutated.mutation_log))

# Evolve one generation
stats = asyncio.run(pool.evolve_generation())
print('Population stats:', stats)

1.5 Mutation Recording

All mutations are recorded with: - Timestamp - Mutation type - Target module - Description - Parameters before/after - Success status

This enables: - Analysis of mutation effectiveness - Debugging architecture changes - Evolutionary history tracking

2. Co-evolution System

2.1 Overview

The Co-evolution System (evospikenet/coevolution.py) enables multiple populations to evolve together through competitive, cooperative, or mixed interactions.

2.2 Key Features

2.2.1 Co-evolution Modes

Competitive Co-evolution: - Predator-prey dynamics - Adversarial training (Generator vs Discriminator style) - Tournament-based matching - Fitness determined by competition outcomes

Cooperative Co-evolution: - Team-based evolution (3+ individuals) - Role specialization (Leader/Follower) - Communication protocol evolution - Shared team rewards

Mixed Mode: - Combines both competitive and cooperative interactions - Balances individual and team fitness - More realistic evolutionary dynamics

2.2.2 Speciation for Diversity

Distance-Based Speciation: - Groups genomes by structural similarity - Uses genomic distance metric - Threshold: 0.3 (configurable)

Fitness-Sharing Speciation: - Groups genomes with similar fitness - Promotes diversity across fitness landscape - Prevents premature convergence

Niche-Based Speciation: - Groups genomes by behavioral similarity - Based on complexity metrics - Enables ecological niches

Species Management: - Removes stagnant species (15 gen threshold) - Maintains species age tracking - Monitors best fitness per species

2.2.3 Large-Scale Population Management

Adaptive Population Sizing: - Adjusts species size based on performance - Min species size: 5 individuals - Max species size: 100 individuals - Total population: up to 1000+ individuals

Resource Allocation: - Distributes offspring based on species fitness - Elite preservation within species - Prevents extinction of promising lineages

2.2.4 Interaction Strategies

Pairing Strategies (Competitive): - Random pairing - Tournament pairing (best opponents) - Best-vs-best pairing

Team Formation (Cooperative): - Random team formation - Role-based team composition - Cross-population teams

2.3 Configuration

@dataclass
class CoevolutionConfig:
    # Population settings
    num_populations: int = 2
    population_size_per_group: int = 50
    max_total_population: int = 1000

    # Co-evolution mode
    mode: CoevolutionMode = CoevolutionMode.COMPETITIVE
    interaction_frequency: int = 5  # Every N generations

    # Competitive settings
    predator_prey_ratio: float = 0.5
    adversarial_matching: str = "tournament"

    # Cooperative settings
    team_size: int = 3
    role_specialization: bool = True
    communication_evolution: bool = True

    # Diversity maintenance
    speciation_method: SpeciationMethod = SpeciationMethod.DISTANCE_BASED
    speciation_threshold: float = 0.3
    niche_capacity: int = 20
    diversity_pressure: float = 0.2

    # Adaptation control
    adaptive_population_size: bool = True
    min_species_size: int = 5
    max_species_size: int = 100

2.4 Usage Example

import asyncio
from evospikenet.genome import create_initial_genome
from evospikenet.coevolution import CoevolutionEngine, CoevolutionConfig, CoevolutionMode

config = CoevolutionConfig(
    num_populations=3,
    population_size_per_group=100,
    mode=CoevolutionMode.MIXED,
    team_size=3
)
engine = CoevolutionEngine(config)

# Initialize populations
initial_genomes = [create_initial_genome() for _ in range(3)]
engine.initialize_populations(initial_genomes)

# Run co-evolution — run_generation() is async
async def run_coevolution():
    for generation in range(100):
        stats = await engine.run_generation()

        # Monitor progress
        print(f"Generation {generation}:")
        for pop_id, stat in stats.items():
            print(f"  {pop_id}: avg_fitness={stat.avg_fitness:.3f}")

        # Get co-evolution statistics
        coevo_stats = engine.get_coevolution_stats()
        print(f"  Total individuals: {coevo_stats['total_individuals']}")
        print(f"  Number of species: {coevo_stats['num_species']}")

    # Export interaction history
    return engine.export_interaction_history()

history = asyncio.run(run_coevolution())

2.5 Interaction Evaluation

Both competitive and cooperative evaluation are fully implemented using real brain instantiation via GenomeToBrainConverter:

Competitive evaluation (_evaluate_competitive_interaction):
Each genome is instantiated as an InstantiatedBrain and run on a shared input stimulus. The forward-pass output norms are compared — the genome producing a higher L2 norm wins the round. Diversity is also rewarded so that genomes exploring distinct activation patterns receive a fitness bonus.

Cooperative evaluation (_evaluate_team_performance):
All genomes in the team are instantiated and run sequentially on the same input. The team score is the sum of individual output norms. A diversity bonus (variance across outputs) is added to encourage role specialisation within the team.

# Simplified view of the implemented logic
from evospikenet.genome_to_brain import GenomeToBrainConverter
import torch

converter = GenomeToBrainConverter()

# Competitive: genome1 vs genome2
brain1 = converter.instantiate(genome1)
brain2 = converter.instantiate(genome2)
stimulus = torch.randn(1, brain1.input_dim)
out1 = brain1(stimulus)
out2 = brain2(stimulus)
winner = "genome1" if out1.norm() > out2.norm() else "genome2"

# Cooperative: shared team stimulus
team_brains = [converter.instantiate(g) for g in team]
team_outputs = [b(stimulus) for b in team_brains]
team_score = sum(o.norm().item() for o in team_outputs)
diversity_bonus = torch.stack(team_outputs).var(dim=0).mean().item()

3. Integration with Existing System

3.1 Genome Compatibility

Both systems are fully compatible with existing EvoGenome structure: - No changes to core genome format - Works with existing fitness evaluators - Compatible with distributed evaluation

3.2 Integration with GenomePool

import asyncio
from evospikenet.genome_pool import GenomePool
from evospikenet.evolution_engine import MutationEngine
from evospikenet.advanced_mutations import AdvancedMutationEngine, AdaptiveMutationConfig
from evospikenet.snapshot_recovery import SnapshotManager

# Build a mutation pipeline with advanced structural mutations
advanced = AdvancedMutationEngine(AdaptiveMutationConfig(base_rate=0.15))
engine = MutationEngine(base_mutation_rate=0.05, advanced_engine=advanced)

# Attach a snapshot manager for automatic per-generation checkpoints
manager = SnapshotManager(snapshot_dir="/tmp/evo_snapshots")
pool = GenomePool(population_size=100, snapshot_manager=manager)
pool.initialize_population()

async def run_evolution(generations: int = 5):
    for gen in range(generations):
        # (Optional) Apply AdvancedMutationEngine to specific genomes directly
        for genome in pool.genomes[:5]:  # top-5 candidates for extra structural search
            mutated = engine.mutate_genome(genome)
            pool.genomes[pool.genomes.index(genome)] = mutated

        # Standard evolution; auto-snapshot fires after each generation
        stats = await pool.evolve_generation()
        print(f"Gen {stats.generation}: avg={stats.avg_fitness:.3f}, max={stats.max_fitness:.3f}")

asyncio.run(run_evolution())

# View recorded snapshots
print("Snapshots:", [s["id"] for s in manager.list_snapshots()])

3.3 Integration with Distributed Brain Nodes

After evolution completes, the best genome can be deployed to live DistributedBrainNode instances via deploy_to_nodes(). Each node instantiates the genome as a real neural network and uses it to drive its forward pass during normal brain simulation.

import asyncio
from evospikenet.distributed_evolution_engine import DistributedEvolutionEngine
from evospikenet.distributed_brain_node import DistributedBrainNode

# Configure and run evolution
engine = DistributedEvolutionEngine(config={"population_size": 50})
best = asyncio.run(engine.run_evolution(generations=50))
print(f"Best genome fitness: {best.fitness_score:.4f}")

# Deploy the winner to distributed brain nodes
pfc_node   = DistributedBrainNode("pfc",   config={"neuron_count": 1000, "specialization": "pfc"})
motor_node = DistributedBrainNode("motor", config={"neuron_count": 512,  "specialization": "motor"})
memory_node = DistributedBrainNode("memory", config={"neuron_count": 768, "specialization": "memory"})

engine.deploy_to_nodes([pfc_node, motor_node, memory_node])

# Verify deployment
for node in [pfc_node, motor_node, memory_node]:
    stats = node.get_stats()
    print(f"{node.node_id}: genome_deployed={stats['genome_deployed']}")

Once deployed, each node's _process_brain_command() will route commands through the genome-driven InstantiatedBrain forward pass, enabling genome-level adaptation of the distributed system.

4. Performance Considerations

4.1 Computational Cost

Structural Mutations: - Layer operations: O(L²) where L = num_layers - Connection mutations: O(L²) - Module mutations: O(M) where M = num_modules - Overall: Lightweight, suitable for online evolution

Co-evolution: - Interaction evaluation: Dominant cost - Speciation: O(N²) where N = population_size - Species management: O(N) - Overall: Scales to 1000+ individuals with proper parallelization

4.2 Memory Usage

Per Genome: - Base genome: ~10 KB - Mutation history: ~1 KB per mutation - Species data: ~2 KB per species

Total System: - 1000 individuals: ~10-15 MB - Interaction history: ~100 KB per 1000 interactions

4.3 Optimization Tips

  1. Parallel Interaction Evaluation: Use async/await for concurrent evaluations
  2. Batch Processing: Evaluate multiple teams/pairs simultaneously
  3. Periodic Cleanup: Remove old mutation history periodically
  4. Lazy Species Update: Update species only when needed
  5. Distributed Co-evolution: Split populations across nodes

5. Testing

5.1 Unit Tests

Advanced Mutations (tests/unit/test_advanced_mutations.py): - Layer addition/removal - Connection topology mutations - Activation function evolution - Module mutations - Configuration validation - Mutation history tracking

Co-evolution (tests/unit/test_coevolution.py): - Population initialization - Competitive evaluation - Cooperative evaluation - Speciation algorithms - Pairing strategies - Team formation - Statistics tracking

5.2 Running Tests

# Run all tests
pytest tests/unit/test_advanced_mutations.py -v
pytest tests/unit/test_coevolution.py -v

# Run specific test
pytest tests/unit/test_coevolution.py::TestCoevolutionEngine::test_competitive_evaluation -v

# With coverage
pytest tests/unit/ --cov=evospikenet.advanced_mutations --cov=evospikenet.coevolution

6. Future Enhancements

6.1 Planned Features

Robot Hardware Integration: - Real-world sensor integration - Actuator control loops - Hardware adaptation - Safety constraints - Energy optimization

Advanced Speciation: - Behavioral speciation (task-specific metrics) - Genetic speciation (gene-level analysis) - Phylogenetic tree tracking

Communication Evolution: - Protocol learning for cooperative teams - Message passing optimization - Bandwidth constraints

Meta-Evolution: - Evolve mutation rates - Evolve selection strategies - Evolve speciation parameters

6.2 Research Directions

  • Multi-objective co-evolution
  • Competitive-cooperative balance learning
  • Evolutionary robotics applications
  • Open-ended evolution
  • Artificial life simulations

7. References

7.1 Key Papers

  1. Stanley, K. O., & Miikkulainen, R. (2002). "Evolving Neural Networks through Augmenting Topologies." Evolutionary Computation, 10(2), 99-127.

  2. Popovici, E., et al. (2012). "Coevolutionary Principles." Handbook of Natural Computing, 987-1033.

  3. Potter, M. A., & De Jong, K. A. (2000). "Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents." Evolutionary Computation, 8(1), 1-29.

7.2 Implementation References

  • evospikenet/genome.py: Core genome structures
  • evospikenet/genome_pool.py: Population management
  • evospikenet/fitness_evaluator.py: Fitness evaluation
  • evospikenet/distributed_evaluation.py: Distributed processing

8. License

Copyright 2026 Moonlight Technologies Inc. All Rights Reserved.

This implementation is part of the EvoSpikeNet L5 Self-Evolution system and is proprietary to Moonlight Technologies Inc.

9. Contact

For questions or issues, contact: - Author: Masahiro Aoki - Email: [contact information] - Project: EvoSpikeNet L5 Self-Evolution

Document Version: 1.0
Last Updated: 2026-01-22