Pipeline dna evolution system

Documentation for pipeline_dna_evolution_system from the Pipeline ex repository.

Pipeline DNA and Evolution System Specification

Introduction

This document details the genetic framework for pipeline evolution, where pipelines are treated as digital organisms with hereditable traits, mutation capabilities, and evolutionary fitness functions.

Pipeline DNA Structure

1. Genetic Blueprint Architecture

defmodule Pipeline.Meta.DNA.Genome do
  @moduledoc """
  The complete genetic blueprint of a pipeline organism
  """
  
  defstruct [
    # Identity genes
    :id,                    # Unique genetic identifier (UUID)
    :lineage,              # Ancestry tracking
    :generation,           # Evolution generation number
    :birth_timestamp,      # Creation time
    
    # Core chromosomes
    :structural_chromosome,  # Pipeline architecture genes
    :behavioral_chromosome,  # Execution pattern genes
    :optimization_chromosome,# Performance tuning genes
    :adaptation_chromosome,  # Environment response genes
    
    # Genetic markers
    :dominant_traits,       # Strongly expressed characteristics
    :recessive_traits,      # Latent characteristics
    :mutations,            # Applied genetic modifications
    :epigenetic_markers,   # Environment-induced changes
    
    # Fitness metrics
    :fitness_score,        # Overall evolutionary fitness
    :survival_rate,        # Success in various environments
    :reproduction_rate,    # Frequency of being selected for breeding
    :innovation_index      # Novel trait generation score
  ]
end

2. Chromosome Definitions

2.1 Structural Chromosome

Controls the fundamental architecture of the pipeline:

defmodule Pipeline.Meta.DNA.StructuralChromosome do
  defstruct [
    # Step sequencing genes
    step_order_gene: %{
      sequence_pattern: :linear,  # :linear, :parallel, :conditional, :recursive
      parallelism_factor: 1.0,    # 0.0 to 1.0
      branching_complexity: 0     # Number of conditional branches
    },
    
    # Component selection genes
    provider_affinity_gene: %{
      claude_preference: 0.5,
      gemini_preference: 0.5,
      provider_switching_threshold: 0.7
    },
    
    # Architecture genes
    modular_design_gene: %{
      component_reuse_rate: 0.0,
      abstraction_level: :medium,  # :low, :medium, :high
      coupling_coefficient: 0.5
    }
  ]
end

2.2 Behavioral Chromosome

Determines execution patterns and runtime behavior:

defmodule Pipeline.Meta.DNA.BehavioralChromosome do
  defstruct [
    # Error handling genes
    resilience_gene: %{
      retry_strategy: :exponential_backoff,
      max_retries: 3,
      failure_tolerance: 0.2,
      self_healing_capability: true
    },
    
    # Resource management genes
    efficiency_gene: %{
      token_conservation: 0.7,    # 0.0 wasteful to 1.0 optimal
      parallel_execution: true,
      batch_processing: false,
      caching_aggressiveness: 0.5
    },
    
    # Adaptation genes
    learning_gene: %{
      feedback_sensitivity: 0.8,
      adaptation_speed: 0.5,
      memory_retention: 0.7,
      pattern_recognition: true
    }
  ]
end

3. Genetic Operators

3.1 Mutation System

# pipelines/meta/evolution/mutation_operator.yaml
name: genetic_mutation_operator
steps:
  - name: select_mutation_targets
    type: claude_smart
    prompt: |
      Analyze pipeline DNA and select mutation targets:
      - Current genome: {{pipeline_dna}}
      - Performance history: {{performance_metrics}}
      - Environmental pressures: {{selection_pressure}}
      
      Identify genes most likely to benefit from mutation.
      
  - name: apply_mutations
    type: claude_extract
    prompt: |
      Apply mutations to selected genes:
      {{steps.select_mutation_targets.result}}
      
      Mutation types:
      1. Point mutations (single gene changes)
      2. Insertions (new capabilities)
      3. Deletions (remove inefficiencies)
      4. Inversions (reorder sequences)
    schema:
      mutations:
        - gene_target
        - mutation_type
        - original_value
        - mutated_value
        - expected_impact

3.2 Crossover Mechanism

# pipelines/meta/evolution/crossover_operator.yaml
name: genetic_crossover_operator
steps:
  - name: parent_compatibility_check
    type: gemini
    prompt: |
      Assess genetic compatibility of parent pipelines:
      - Parent 1 DNA: {{parent1_dna}}
      - Parent 2 DNA: {{parent2_dna}}
      
      Determine:
      - Compatibility score
      - Optimal crossover points
      - Trait dominance patterns
      
  - name: perform_crossover
    type: claude_robust
    prompt: |
      Execute genetic crossover between parents:
      {{steps.parent_compatibility_check.result}}
      
      Crossover strategies:
      1. Single-point crossover
      2. Multi-point crossover
      3. Uniform crossover
      4. Adaptive crossover based on trait performance
      
  - name: offspring_validation
    type: claude_smart
    prompt: |
      Validate offspring viability:
      {{steps.perform_crossover.result}}
      
      Check for:
      - Genetic integrity
      - Lethal combinations
      - Hybrid vigor potential

4. Evolution Environment

4.1 Selection Pressure Mechanisms

defmodule Pipeline.Meta.Evolution.SelectionPressure do
  @moduledoc """
  Environmental factors that drive pipeline evolution
  """
  
  def calculate_fitness(pipeline_dna, environment) do
    base_fitness = calculate_base_fitness(pipeline_dna)
    
    # Apply environmental modifiers
    fitness = base_fitness
    |> apply_performance_pressure(environment.performance_requirements)
    |> apply_resource_pressure(environment.resource_constraints)
    |> apply_complexity_pressure(environment.complexity_tolerance)
    |> apply_innovation_bonus(pipeline_dna.innovation_index)
    
    # Epigenetic factors
    fitness * calculate_epigenetic_modifier(pipeline_dna, environment)
  end
  
  defp apply_performance_pressure(fitness, requirements) do
    # Favor pipelines that meet performance targets
    fitness * performance_multiplier(requirements)
  end
  
  defp apply_innovation_bonus(fitness, innovation_index) do
    # Reward novel solutions
    fitness * (1 + innovation_index * 0.2)
  end
end

4.2 Population Dynamics

# pipelines/meta/evolution/population_manager.yaml
name: evolutionary_population_manager
steps:
  - name: population_census
    type: gemini_instructor
    prompt: Analyze current pipeline population
    functions:
      - name: calculate_genetic_diversity
        description: Measure genetic variation in population
      - name: identify_ecological_niches
        description: Find specialized pipeline roles
        
  - name: selection_process
    type: claude_smart
    prompt: |
      Select pipelines for next generation:
      {{steps.population_census.result}}
      
      Selection methods:
      1. Tournament selection (competitive)
      2. Roulette wheel (fitness-proportionate)
      3. Elitism (preserve best performers)
      4. Diversity preservation (maintain variety)
      
  - name: population_regulation
    type: claude_robust
    prompt: |
      Regulate population size and diversity:
      {{steps.selection_process.result}}
      
      Actions:
      - Cull underperformers
      - Promote high-fitness individuals
      - Introduce random immigrants
      - Maintain genetic diversity

5. Epigenetic System

5.1 Environmental Adaptation

# pipelines/meta/evolution/epigenetic_adaptation.yaml
name: epigenetic_modification_system
steps:
  - name: environment_sensing
    type: gemini
    prompt: |
      Analyze environmental conditions:
      - Current workload patterns: {{workload_data}}
      - Resource availability: {{resource_metrics}}
      - Error patterns: {{error_analysis}}
      
  - name: epigenetic_marking
    type: claude_extract
    prompt: |
      Apply epigenetic markers based on environment:
      {{steps.environment_sensing.result}}
      
      Modifiable traits:
      - Prompt aggressiveness
      - Error tolerance
      - Resource usage
      - Parallelization
    schema:
      epigenetic_changes:
        - trait_name
        - modification_type
        - magnitude
        - duration
        
  - name: trait_expression_update
    type: claude_smart
    prompt: |
      Update trait expression based on epigenetic markers:
      {{steps.epigenetic_marking.result}}
      
      Create reversible modifications that allow
      rapid adaptation without genetic changes.

6. Evolutionary Algorithms

6.1 Genetic Algorithm Implementation

defmodule Pipeline.Meta.Evolution.GeneticAlgorithm do
  @population_size 100
  @elite_count 10
  @mutation_rate 0.1
  @crossover_rate 0.7
  @max_generations 1000
  
  def evolve(initial_population, fitness_function, target_fitness) do
    Stream.iterate({initial_population, 0}, fn {population, generation} ->
      # Evaluate fitness
      evaluated = evaluate_population(population, fitness_function)
      
      # Check termination condition
      best = Enum.max_by(evaluated, & &1.fitness)
      if best.fitness >= target_fitness do
        {:halt, best}
      else
        # Selection
        selected = selection(evaluated)
        
        # Reproduction
        offspring = reproduce(selected)
        
        # Mutation
        mutated = mutate(offspring, @mutation_rate)
        
        # Elite preservation
        elite = Enum.take(evaluated, @elite_count)
        new_population = elite ++ mutated
        
        {new_population, generation + 1}
      end
    end)
    |> Stream.take_while(fn
      {:halt, _} -> false
      {_, gen} -> gen < @max_generations
    end)
    |> Enum.to_list()
    |> List.last()
  end
end

6.2 Evolutionary Strategies

# pipelines/meta/evolution/evolutionary_strategies.yaml
name: advanced_evolutionary_strategies
steps:
  - name: strategy_selection
    type: claude_smart
    prompt: |
      Select optimal evolutionary strategy based on:
      - Population characteristics: {{population_analysis}}
      - Performance goals: {{evolution_targets}}
      - Time constraints: {{time_budget}}
      
      Strategies:
      1. (μ,λ)-ES: Generate λ offspring from μ parents
      2. (μ+λ)-ES: Select from parents and offspring
      3. CMA-ES: Covariance Matrix Adaptation
      4. NEAT: NeuroEvolution of Augmenting Topologies
      
  - name: strategy_implementation
    type: claude_robust
    prompt: |
      Implement selected evolutionary strategy:
      {{steps.strategy_selection.result}}
      
      Optimize for:
      - Convergence speed
      - Solution quality
      - Diversity maintenance
      - Computational efficiency

7. Phylogenetic Tracking

7.1 Lineage Recording

defmodule Pipeline.Meta.Evolution.Phylogeny do
  @moduledoc """
  Track evolutionary relationships between pipelines
  """
  
  defstruct [
    :tree_root,
    :branches,
    :extinction_events,
    :speciation_points,
    :convergent_evolution_cases
  ]
  
  def record_birth(parent_ids, offspring_dna) do
    %{
      id: offspring_dna.id,
      parents: parent_ids,
      birth_time: DateTime.utc_now(),
      generation: calculate_generation(parent_ids),
      mutations: offspring_dna.mutations,
      initial_fitness: nil
    }
  end
  
  def trace_ancestry(pipeline_id, phylogeny) do
    # Recursively trace back through generations
    # Returns complete lineage history
  end
end

8. Implementation Examples

8.1 Complete Evolution Cycle

# pipelines/meta/evolution/complete_evolution_cycle.yaml
name: full_evolutionary_cycle
steps:
  - name: population_initialization
    type: claude_batch
    prompts:
      - "Generate diverse pipeline genome 1"
      - "Generate diverse pipeline genome 2"
      - "Generate diverse pipeline genome 3"
      # ... up to population size
      
  - name: fitness_evaluation
    type: parallel_pipeline_executor
    config:
      test_suite: "evolution_fitness_tests"
      metrics: ["speed", "accuracy", "efficiency", "robustness"]
      
  - name: selection_and_reproduction
    type: claude_smart
    prompt: |
      Perform selection and reproduction:
      - Population fitness: {{steps.fitness_evaluation.results}}
      - Selection pressure: {{environment.selection_pressure}}
      
      Apply:
      1. Tournament selection
      2. Genetic crossover
      3. Mutation operations
      
  - name: next_generation_deployment
    type: pipeline_deployer
    config:
      deploy_strategy: "gradual_replacement"
      monitoring_enabled: true

Conclusion

The Pipeline DNA and Evolution System provides a robust framework for treating pipelines as evolving digital organisms. Through genetic encoding, mutation, crossover, and selection pressure, pipelines can adapt and improve over generations, discovering novel solutions and optimizing for changing environments.

This biological approach to pipeline development enables:

Automatic optimization without manual intervention
Discovery of non-obvious solutions through mutation
Preservation of successful traits across generations
Rapid adaptation to changing requirements
Emergent complexity from simple genetic rules

The system creates a living ecosystem of pipelines that continuously evolve to meet user needs more effectively.