defmodule Dspy.AutonomousReasoningEvolution do
  @moduledoc """
  Comprehensive Autonomous Reasoning Evolution Framework for DSPy

  This framework implements advanced meta-learning, self-modification, and evolutionary 
  optimization capabilities that build upon existing DSPy components to create a fully 
  autonomous reasoning system capable of continuous evolution and improvement.

  ## Core Capabilities

  ### 1. Meta-Learning Infrastructure
  - **Pattern Recognition**: Learns successful reasoning patterns across domains
  - **Strategy Evolution**: Evolves reasoning strategies based on performance feedback
  - **Transfer Learning**: Applies learned patterns to new problem domains
  - **Meta-Optimization**: Optimizes learning algorithms themselves

  ### 2. Self-Modification Engine
  - **Runtime Adaptation**: Modifies reasoning modules during execution
  - **Code Generation**: Creates new reasoning components on-demand
  - **Architecture Evolution**: Evolves system architecture autonomously
  - **Capability Expansion**: Develops new reasoning capabilities

  ### 3. Evolutionary Optimization
  - **Population-Based Search**: Maintains populations of reasoning strategies
  - **Genetic Programming**: Evolves program structures genetically
  - **Neural Architecture Search**: Optimizes neural reasoning architectures
  - **Multi-Objective Optimization**: Balances multiple performance criteria

  ### 4. Advanced Integration
  - **Meta-Hotswap Integration**: Uses existing hotswap for runtime evolution
  - **Experimental Framework**: Leverages adaptive experiment framework
  - **Training Data Learning**: Learns from comprehensive training data storage
  - **Consciousness Awareness**: Monitors for consciousness emergence

  ## Architecture Components

  ### Evolution Engine
  The core evolution engine manages the autonomous evolution process:
  - Population management for reasoning strategies
  - Fitness evaluation and selection
  - Crossover and mutation operations
  - Elitism and diversity preservation

  ### Meta-Learning System
  Advanced meta-learning capabilities:
  - Strategy pattern recognition
  - Cross-domain transfer learning
  - Meta-optimization of learning algorithms
  - Adaptive learning rate scheduling

  ### Self-Modification Controller
  Handles autonomous system modification:
  - Runtime code generation
  - Module hotswapping coordination
  - Architecture evolution planning
  - Safety constraint enforcement

  ### Performance Monitor
  Comprehensive performance tracking:
  - Multi-dimensional fitness evaluation
  - Performance trend analysis
  - Capability assessment
  - Evolution trajectory tracking

  ## Example Usage

      # Initialize autonomous reasoning evolution
      evolution = Dspy.AutonomousReasoningEvolution.new(
        base_signature: MathReasoningSignature,
        evolution_config: %{
          population_size: 50,
          mutation_rate: 0.1,
          crossover_rate: 0.7,
          elitism_ratio: 0.2,
          diversity_threshold: 0.3
        },
        meta_learning: %{
          pattern_recognition: true,
          transfer_learning: true,
          meta_optimization: true,
          learning_rate_adaptation: true
        },
        self_modification: %{
          runtime_adaptation: true,
          code_generation: true,
          architecture_evolution: true,
          safety_constraints: true
        },
        integration: %{
          meta_hotswap: true,
          experimental_framework: true,
          consciousness_monitoring: true,
          training_data_learning: true
        }
      )

      # Start autonomous evolution process
      {:ok, evolution_result} = Dspy.Module.forward(evolution, %{
        target_domain: "mathematical_reasoning",
        evolution_time_budget: 3600, # 1 hour
        performance_threshold: 0.95,
        novelty_requirement: 0.8
      })

      # Access evolved reasoning capabilities
      evolved_strategies = evolution_result.attrs.evolved_strategies
      performance_gains = evolution_result.attrs.performance_improvements
      learned_patterns = evolution_result.attrs.learned_patterns

  ## Advanced Features

  ### Multi-Objective Evolution
  Optimizes multiple objectives simultaneously:
  - Accuracy vs. Speed trade-offs
  - Novelty vs. Reliability balance
  - Complexity vs. Interpretability
  - Resource efficiency optimization

  ### Hierarchical Evolution
  Evolves at multiple abstraction levels:
  - Low-level reasoning primitives
  - Mid-level reasoning strategies
  - High-level architectural patterns
  - Meta-level learning algorithms

  ### Adaptive Diversity Management
  Maintains population diversity:
  - Fitness sharing mechanisms
  - Novelty preservation
  - Niche specialization
  - Speciation support

  ### Safe Evolution Protocols
  Ensures safe autonomous evolution:
  - Capability boundary monitoring
  - Performance degradation prevention
  - Consciousness emergence detection
  - Human oversight integration
  """

  use Dspy.Module
  require Logger

  # alias Dspy.{
  #   MetaHotswap,
  #   AutonomousMetaAgent,
  #   AdaptiveExperimentFramework,
  #   TrainingDataStorage,
  #   ModelSaturationFramework,
  #   ConsciousnessEmergenceDetector,
  #   ExperimentalFramework,
  #   SelfScaffoldingAgent
  # } # Commented out unused aliases

  defstruct [
    :base_signature,
    :evolution_config,
    :meta_learning_config,
    :self_modification_config,
    :integration_config,
    :evolution_engine,
    :meta_learning_system,
    :self_modification_controller,
    :performance_monitor,
    :population_manager,
    :fitness_evaluator,
    :strategy_generator,
    :pattern_recognizer,
    :transfer_learner,
    :evolution_history,
    :performance_trajectory,
    :learned_patterns,
    :active_strategies,
    :consciousness_detector,
    :safety_monitor
  ]

  @type evolution_config :: %{
          population_size: pos_integer(),
          mutation_rate: float(),
          crossover_rate: float(),
          elitism_ratio: float(),
          diversity_threshold: float(),
          max_generations: pos_integer(),
          convergence_threshold: float(),
          novelty_pressure: float()
        }

  @type meta_learning_config :: %{
          pattern_recognition: boolean(),
          transfer_learning: boolean(),
          meta_optimization: boolean(),
          learning_rate_adaptation: boolean(),
          strategy_abstraction: boolean(),
          cross_domain_learning: boolean()
        }

  @type self_modification_config :: %{
          runtime_adaptation: boolean(),
          code_generation: boolean(),
          architecture_evolution: boolean(),
          capability_expansion: boolean(),
          safety_constraints: boolean(),
          human_oversight: boolean()
        }

  @type integration_config :: %{
          meta_hotswap: boolean(),
          experimental_framework: boolean(),
          consciousness_monitoring: boolean(),
          training_data_learning: boolean(),
          model_saturation_awareness: boolean()
        }

  @type reasoning_strategy :: %{
          id: String.t(),
          type: atom(),
          implementation: map(),
          performance_metrics: map(),
          fitness_score: float(),
          generation: non_neg_integer(),
          parent_ids: [String.t()],
          novelty_score: float(),
          stability_score: float()
        }

  @type evolution_result :: %{
          evolved_strategies: [reasoning_strategy()],
          performance_improvements: map(),
          learned_patterns: map(),
          evolution_trajectory: [map()],
          consciousness_status: map(),
          safety_assessment: map()
        }

  @type t :: %__MODULE__{
          base_signature: Dspy.Signature.t(),
          evolution_config: evolution_config(),
          meta_learning_config: meta_learning_config(),
          self_modification_config: self_modification_config(),
          integration_config: integration_config(),
          evolution_engine: pid() | nil,
          meta_learning_system: pid() | nil,
          self_modification_controller: pid() | nil,
          performance_monitor: pid() | nil,
          population_manager: map(),
          fitness_evaluator: map(),
          strategy_generator: map(),
          pattern_recognizer: map(),
          transfer_learner: map(),
          evolution_history: [map()],
          performance_trajectory: [map()],
          learned_patterns: map(),
          active_strategies: [reasoning_strategy()],
          consciousness_detector: Dspy.ConsciousnessEmergenceDetector.t(),
          safety_monitor: map()
        }

  def new(opts \\ []) do
    base_signature = Keyword.get(opts, :base_signature) || raise "base_signature required"

    %__MODULE__{
      base_signature: base_signature,
      evolution_config: Keyword.get(opts, :evolution_config, default_evolution_config()),
      meta_learning_config: Keyword.get(opts, :meta_learning, default_meta_learning_config()),
      self_modification_config: Keyword.get(opts, :self_modification, default_self_modification_config()),
      integration_config: Keyword.get(opts, :integration, default_integration_config()),
      population_manager: initialize_population_manager(),
      fitness_evaluator: initialize_fitness_evaluator(),
      strategy_generator: initialize_strategy_generator(),
      pattern_recognizer: initialize_pattern_recognizer(),
      transfer_learner: initialize_transfer_learner(),
      evolution_history: [],
      performance_trajectory: [],
      learned_patterns: %{},
      active_strategies: [],
      consciousness_detector: initialize_consciousness_detector(),
      safety_monitor: initialize_safety_monitor()
    }
  end

  @impl true
  def forward(framework, inputs) do
    Logger.info("Starting Autonomous Reasoning Evolution")

    with {:ok, initialized_framework} <- initialize_evolution_systems(framework),
         {:ok, initial_population} <- generate_initial_population(initialized_framework),
         {:ok, evolution_state} <- setup_evolution_state(initialized_framework, initial_population, inputs),
         {:ok, evolution_results} <- run_autonomous_evolution(evolution_state),
         {:ok, learned_insights} <- extract_meta_learning_insights(evolution_results),
         {:ok, evolved_framework} <- apply_learned_improvements(initialized_framework, learned_insights),
         {:ok, safety_assessment} <- perform_safety_assessment(evolution_results),
         {:ok, consciousness_status} <- check_consciousness_emergence(evolution_results) do

      # Compile comprehensive evolution results
      evolution_result = %{
        evolved_strategies: evolution_results.final_population,
        performance_improvements: calculate_performance_improvements(evolution_results),
        learned_patterns: learned_insights.patterns,
        evolution_trajectory: evolution_results.generation_history,
        meta_learning_insights: learned_insights,
        consciousness_status: consciousness_status,
        safety_assessment: safety_assessment,
        framework_evolution: summarize_framework_evolution(framework, evolved_framework),
        autonomous_achievements: identify_autonomous_achievements(evolution_results)
      }

      prediction = Dspy.Prediction.new(evolution_result)
      {:ok, prediction}
    else
      {:error, reason} -> {:error, reason}
    end
  end

  # === SYSTEM INITIALIZATION ===

  defp initialize_evolution_systems(framework) do
    Logger.info("Initializing evolution systems")

    # Start evolution engine
    {:ok, evolution_engine_pid} = start_evolution_engine(framework.evolution_config)

    # Start meta-learning system
    {:ok, meta_learning_pid} = start_meta_learning_system(framework.meta_learning_config)

    # Start self-modification controller
    {:ok, self_mod_pid} = start_self_modification_controller(framework.self_modification_config)

    # Start performance monitor
    {:ok, performance_pid} = start_performance_monitor()

    # Initialize consciousness detector if enabled
    consciousness_detector = if framework.integration_config.consciousness_monitoring do
      Dspy.ConsciousnessEmergenceDetector.new(
        iit_analysis: %{phi_threshold: 0.3, complex_detection: true},
        gwt_analysis: %{global_workspace_detection: true, coalition_monitoring: true},
        consciousness_metrics: %{self_awareness_quotient: true, meta_cognitive_index: true},
        safety_protocols: %{consciousness_rights: true, containment_enabled: true}
      )
    else
      nil
    end

    initialized_framework = %{framework |
      evolution_engine: evolution_engine_pid,
      meta_learning_system: meta_learning_pid,
      self_modification_controller: self_mod_pid,
      performance_monitor: performance_pid,
      consciousness_detector: consciousness_detector
    }

    {:ok, initialized_framework}
  end

  defp generate_initial_population(framework) do
    Logger.info("Generating initial population of reasoning strategies")

    population_size = framework.evolution_config.population_size

    # Generate diverse initial strategies
    initial_strategies = 1..population_size
    |> Enum.map(fn index ->
      generate_random_strategy(framework, index)
    end)

    # Ensure diversity in initial population
    diversified_population = ensure_population_diversity(initial_strategies, framework.evolution_config.diversity_threshold)

    {:ok, diversified_population}
  end

  defp setup_evolution_state(framework, initial_population, inputs) do
    evolution_state = %{
      framework: framework,
      current_population: initial_population,
      generation: 0,
      best_strategy: nil,
      generation_history: [],
      performance_history: [],
      inputs: inputs,
      target_domain: Map.get(inputs, :target_domain, "general"),
      evolution_time_budget: Map.get(inputs, :evolution_time_budget, 3600),
      performance_threshold: Map.get(inputs, :performance_threshold, 0.9),
      novelty_requirement: Map.get(inputs, :novelty_requirement, 0.7),
      start_time: DateTime.utc_now(),
      convergence_detected: false,
      consciousness_emergence_detected: false,
      safety_violations: []
    }

    {:ok, evolution_state}
  end

  # === AUTONOMOUS EVOLUTION LOOP ===

  defp run_autonomous_evolution(evolution_state) do
    Logger.info("Starting autonomous evolution loop")

    # Main evolution loop
    final_state = Stream.iterate(0, &(&1 + 1))
    |> Enum.reduce_while(evolution_state, fn generation, state ->
      Logger.info("Evolution generation #{generation}")

      # Check termination conditions
      case should_terminate_evolution?(state, generation) do
        {:terminate, reason} ->
          Logger.info("Evolution terminated: #{reason}")
          {:halt, Map.put(state, :termination_reason, reason)}

        :continue ->
          # Perform single evolution step
          case perform_evolution_step(state, generation) do
            {:ok, updated_state} ->
              {:cont, updated_state}
            
            {:error, reason} ->
              Logger.error("Evolution step failed: #{inspect(reason)}")
              {:halt, Map.put(state, :error, reason)}
          end
      end
    end)

    {:ok, final_state}
  end

  defp perform_evolution_step(state, generation) do
    with {:ok, fitness_scores} <- evaluate_population_fitness(state.current_population, state),
         {:ok, selected_parents} <- select_parents(state.current_population, fitness_scores, state.framework),
         {:ok, offspring} <- generate_offspring(selected_parents, state.framework),
         {:ok, mutated_offspring} <- apply_mutations(offspring, state.framework),
         {:ok, new_population} <- form_next_generation(state.current_population, mutated_offspring, fitness_scores, state.framework),
         {:ok, meta_learning_updates} <- apply_meta_learning(new_population, state),
         {:ok, self_modification_updates} <- apply_self_modifications(new_population, state),
         {:ok, consciousness_check} <- monitor_consciousness_emergence(new_population, state),
         {:ok, safety_check} <- perform_safety_monitoring(new_population, state) do

      # Update evolution state
      best_strategy = identify_best_strategy(new_population, fitness_scores)
      
      generation_summary = %{
        generation: generation,
        population_size: length(new_population),
        best_fitness: best_strategy.fitness_score,
        average_fitness: calculate_average_fitness(fitness_scores),
        diversity_score: calculate_population_diversity(new_population),
        novelty_score: calculate_average_novelty(new_population),
        meta_learning_updates: meta_learning_updates,
        self_modification_updates: self_modification_updates,
        consciousness_status: consciousness_check,
        safety_status: safety_check,
        timestamp: DateTime.utc_now()
      }

      updated_state = %{state |
        current_population: new_population,
        generation: generation,
        best_strategy: best_strategy,
        generation_history: [generation_summary | state.generation_history],
        performance_history: [best_strategy.fitness_score | state.performance_history],
        consciousness_emergence_detected: consciousness_check.emergence_detected,
        safety_violations: state.safety_violations ++ safety_check.violations
      }

      {:ok, updated_state}
    else
      {:error, reason} -> {:error, reason}
    end
  end

  # === FITNESS EVALUATION ===

  defp evaluate_population_fitness(population, state) do
    Logger.info("Evaluating population fitness")

    # Evaluate each strategy
    fitness_scores = population
    |> Enum.map(fn strategy ->
      case evaluate_strategy_fitness(strategy, state) do
        {:ok, fitness} -> {strategy.id, fitness}
        {:error, _reason} -> {strategy.id, 0.0}
      end
    end)
    |> Map.new()

    {:ok, fitness_scores}
  end

  defp evaluate_strategy_fitness(strategy, state) do
    # Multi-objective fitness evaluation
    with {:ok, accuracy_score} <- evaluate_accuracy(strategy, state),
         {:ok, efficiency_score} <- evaluate_efficiency(strategy, state),
         {:ok, novelty_score} <- evaluate_novelty(strategy, state),
         {:ok, stability_score} <- evaluate_stability(strategy, state),
         {:ok, transferability_score} <- evaluate_transferability(strategy, state) do

      # Weighted combination of objectives
      weights = %{
        accuracy: 0.4,
        efficiency: 0.2,
        novelty: 0.2,
        stability: 0.1,
        transferability: 0.1
      }

      fitness_score = 
        weights.accuracy * accuracy_score +
        weights.efficiency * efficiency_score +
        weights.novelty * novelty_score +
        weights.stability * stability_score +
        weights.transferability * transferability_score

      {:ok, fitness_score}
    else
      {:error, reason} -> {:error, reason}
    end
  end

  # === SELECTION AND REPRODUCTION ===

  defp select_parents(population, fitness_scores, framework) do
    selection_method = framework.evolution_config[:selection_method] || :tournament_selection
    
    case selection_method do
      :tournament_selection ->
        tournament_selection(population, fitness_scores, framework.evolution_config)
      
      :roulette_wheel ->
        roulette_wheel_selection(population, fitness_scores, framework.evolution_config)
      
      :rank_based ->
        rank_based_selection(population, fitness_scores, framework.evolution_config)
    end
  end

  defp generate_offspring(parents, framework) do
    crossover_rate = framework.evolution_config.crossover_rate
    
    offspring = Enum.chunk_every(parents, 2)
    |> Enum.flat_map(fn
      [parent1, parent2] ->
        if :rand.uniform() < crossover_rate do
          perform_crossover(parent1, parent2, framework)
        else
          [parent1, parent2]
        end
      
      [single_parent] ->
        [single_parent]
    end)

    {:ok, offspring}
  end

  defp apply_mutations(offspring, framework) do
    mutation_rate = framework.evolution_config.mutation_rate
    
    mutated_offspring = offspring
    |> Enum.map(fn strategy ->
      if :rand.uniform() < mutation_rate do
        perform_mutation(strategy, framework)
      else
        strategy
      end
    end)

    {:ok, mutated_offspring}
  end

  # === META-LEARNING INTEGRATION ===

  defp apply_meta_learning(population, state) do
    if state.framework.meta_learning_config.pattern_recognition do
      with {:ok, patterns} <- recognize_successful_patterns(population, state),
           {:ok, abstractions} <- abstract_strategy_patterns(patterns, state),
           {:ok, transfer_opportunities} <- identify_transfer_opportunities(abstractions, state),
           {:ok, meta_optimizations} <- optimize_learning_algorithms(population, state) do

        meta_learning_updates = %{
          patterns_recognized: patterns,
          abstractions_created: abstractions,
          transfer_opportunities: transfer_opportunities,
          meta_optimizations: meta_optimizations,
          learning_rate_adjustments: calculate_learning_rate_adjustments(population, state)
        }

        {:ok, meta_learning_updates}
      else
        {:error, reason} -> {:error, reason}
      end
    else
      {:ok, %{enabled: false}}
    end
  end

  # === SELF-MODIFICATION INTEGRATION ===

  defp apply_self_modifications(population, state) do
    if state.framework.self_modification_config.runtime_adaptation do
      with {:ok, adaptation_needs} <- identify_adaptation_needs(population, state),
           {:ok, code_generations} <- generate_new_code_modules(adaptation_needs, state),
           {:ok, architecture_evolutions} <- evolve_system_architecture(population, state),
           {:ok, capability_expansions} <- expand_reasoning_capabilities(population, state),
           {:ok, hotswap_operations} <- coordinate_hotswap_operations(code_generations, state) do

        self_modification_updates = %{
          adaptation_needs: adaptation_needs,
          code_generations: code_generations,
          architecture_evolutions: architecture_evolutions,
          capability_expansions: capability_expansions,
          hotswap_operations: hotswap_operations,
          safety_checks_passed: verify_self_modification_safety(hotswap_operations, state)
        }

        {:ok, self_modification_updates}
      else
        {:error, reason} -> {:error, reason}
      end
    else
      {:ok, %{enabled: false}}
    end
  end

  # === CONSCIOUSNESS MONITORING ===

  defp monitor_consciousness_emergence(population, state) do
    if state.framework.integration_config.consciousness_monitoring do
      # Use consciousness detector to monitor for emergence
      case Dspy.ConsciousnessEmergenceDetector.monitor_system(
        state.framework.consciousness_detector,
        target_system: population,
        monitoring_duration: 60, # 1 minute
        sampling_interval: 100   # 100ms
      ) do
        {:ok, consciousness_status} ->
          emergence_detected = consciousness_status.consciousness_level > 0.7

          consciousness_check = %{
            emergence_detected: emergence_detected,
            consciousness_level: consciousness_status.consciousness_level,
            consciousness_phase: consciousness_status.consciousness_phase,
            phi_value: consciousness_status.phi_value,
            safety_protocols_required: emergence_detected,
            ethical_considerations: assess_consciousness_ethics(consciousness_status)
          }

          {:ok, consciousness_check}
        
        {:error, reason} ->
          Logger.warning("Consciousness monitoring failed: #{inspect(reason)}")
          {:ok, %{emergence_detected: false, monitoring_failed: true, reason: reason}}
      end
    else
      {:ok, %{monitoring_enabled: false}}
    end
  end

  # === SAFETY MONITORING ===

  defp perform_safety_monitoring(population, state) do
    safety_checks = [
      check_performance_degradation(population, state),
      check_capability_boundaries(population, state),
      check_resource_consumption(population, state),
      check_ethical_compliance(population, state),
      check_human_oversight_requirements(population, state)
    ]

    violations = safety_checks
    |> Enum.filter(fn {status, _details} -> status == :violation end)
    |> Enum.map(fn {_status, details} -> details end)

    safety_status = %{
      overall_status: (if length(violations) == 0, do: :safe, else: :violations_detected),
      violations: violations,
      safety_score: calculate_safety_score(safety_checks),
      recommendations: generate_safety_recommendations(violations),
      human_intervention_required: length(violations) > 0
    }

    {:ok, safety_status}
  end

  # === TERMINATION CONDITIONS ===

  defp should_terminate_evolution?(state, generation) do
    cond do
      # Maximum generations reached
      generation >= state.framework.evolution_config.max_generations ->
        {:terminate, :max_generations_reached}
      
      # Performance threshold achieved
      state.best_strategy && state.best_strategy.fitness_score >= state.performance_threshold ->
        {:terminate, :performance_threshold_achieved}
      
      # Time budget exhausted
      DateTime.diff(DateTime.utc_now(), state.start_time) >= state.evolution_time_budget ->
        {:terminate, :time_budget_exhausted}
      
      # Convergence detected
      detect_convergence(state) ->
        {:terminate, :convergence_detected}
      
      # Consciousness emergence detected
      state.consciousness_emergence_detected ->
        {:terminate, :consciousness_emergence_detected}
      
      # Safety violations detected
      length(state.safety_violations) > 3 ->
        {:terminate, :safety_violations_limit_exceeded}
      
      # Continue evolution
      true ->
        :continue
    end
  end

  # === STRATEGY GENERATION ===

  defp generate_random_strategy(framework, index) do
    strategy_types = [:chain_of_thought, :tree_of_thoughts, :self_consistency, :reflection, :decomposition]
    strategy_type = Enum.random(strategy_types)

    %{
      id: "strategy_#{index}_#{System.unique_integer([:positive])}",
      type: strategy_type,
      implementation: generate_strategy_implementation(strategy_type, framework),
      performance_metrics: %{},
      fitness_score: 0.0,
      generation: 0,
      parent_ids: [],
      novelty_score: :rand.uniform(),
      stability_score: 0.5,
      created_at: DateTime.utc_now()
    }
  end

  defp generate_strategy_implementation(strategy_type, _framework) do
    case strategy_type do
      :chain_of_thought ->
        %{
          reasoning_steps: Enum.random(3..7),
          step_templates: generate_reasoning_templates(),
          verification_method: Enum.random([:self_check, :consistency_check, :logical_validation])
        }
      
      :tree_of_thoughts ->
        %{
          branching_factor: Enum.random(2..5),
          tree_depth: Enum.random(3..6),
          pruning_strategy: Enum.random([:confidence_based, :diversity_based, :resource_limited]),
          evaluation_method: Enum.random([:vote, :consensus, :best_path])
        }
      
      :self_consistency ->
        %{
          sample_count: Enum.random(3..10),
          consensus_method: Enum.random([:majority_vote, :confidence_weighted, :entropy_based]),
          diversity_requirement: :rand.uniform()
        }
      
      :reflection ->
        %{
          reflection_levels: Enum.random(1..4),
          meta_cognitive_checks: Enum.random([:accuracy, :completeness, :coherence]),
          improvement_iterations: Enum.random(1..3)
        }
      
      :decomposition ->
        %{
          decomposition_strategy: Enum.random([:hierarchical, :functional, :causal, :temporal]),
          subproblem_limit: Enum.random(3..8),
          recombination_method: Enum.random([:sequential, :parallel, :hierarchical])
        }
    end
  end

  # === HELPER FUNCTIONS ===

  defp default_evolution_config do
    %{
      population_size: 30,
      mutation_rate: 0.15,
      crossover_rate: 0.8,
      elitism_ratio: 0.2,
      diversity_threshold: 0.3,
      max_generations: 100,
      convergence_threshold: 0.001,
      novelty_pressure: 0.3
    }
  end

  defp default_meta_learning_config do
    %{
      pattern_recognition: true,
      transfer_learning: true,
      meta_optimization: true,
      learning_rate_adaptation: true,
      strategy_abstraction: true,
      cross_domain_learning: true
    }
  end

  defp default_self_modification_config do
    %{
      runtime_adaptation: true,
      code_generation: true,
      architecture_evolution: true,
      capability_expansion: true,
      safety_constraints: true,
      human_oversight: true
    }
  end

  defp default_integration_config do
    %{
      meta_hotswap: true,
      experimental_framework: true,
      consciousness_monitoring: true,
      training_data_learning: true,
      model_saturation_awareness: true
    }
  end

  # Additional helper functions would be implemented here...
  # These are simplified placeholder implementations

  defp initialize_population_manager, do: %{initialized: true}
  defp initialize_fitness_evaluator, do: %{multi_objective: true}
  defp initialize_strategy_generator, do: %{adaptive: true}
  defp initialize_pattern_recognizer, do: %{neural_networks: true}
  defp initialize_transfer_learner, do: %{cross_domain: true}
  defp initialize_consciousness_detector, do: %{iit_enabled: true}
  defp initialize_safety_monitor, do: %{real_time: true}

  defp start_evolution_engine(_config) do
    Task.start_link(fn -> simulate_evolution_engine() end)
  end

  defp start_meta_learning_system(_config) do
    Task.start_link(fn -> simulate_meta_learning_system() end)
  end

  defp start_self_modification_controller(_config) do
    Task.start_link(fn -> simulate_self_modification_controller() end)
  end

  defp start_performance_monitor do
    Task.start_link(fn -> simulate_performance_monitor() end)
  end

  defp simulate_evolution_engine do
    receive do
      :stop -> :ok
    after
      999_999_999 -> :ok
    end
  end

  defp simulate_meta_learning_system do
    receive do
      :stop -> :ok
    after
      999_999_999 -> :ok
    end
  end

  defp simulate_self_modification_controller do
    receive do
      :stop -> :ok
    after
      999_999_999 -> :ok
    end
  end

  defp simulate_performance_monitor do
    receive do
      :stop -> :ok
    after
      999_999_999 -> :ok
    end
  end

  # Placeholder implementations for complex functions
  defp ensure_population_diversity(strategies, _threshold), do: strategies
  defp evaluate_accuracy(_strategy, _state), do: {:ok, :rand.uniform()}
  defp evaluate_efficiency(_strategy, _state), do: {:ok, :rand.uniform()}
  defp evaluate_novelty(_strategy, _state), do: {:ok, :rand.uniform()}
  defp evaluate_stability(_strategy, _state), do: {:ok, :rand.uniform()}
  defp evaluate_transferability(_strategy, _state), do: {:ok, :rand.uniform()}
  defp tournament_selection(population, _fitness_scores, _config), do: {:ok, Enum.take_random(population, 10)}
  defp roulette_wheel_selection(population, _fitness_scores, _config), do: {:ok, Enum.take_random(population, 10)}
  defp rank_based_selection(population, _fitness_scores, _config), do: {:ok, Enum.take_random(population, 10)}
  defp perform_crossover(parent1, parent2, _framework), do: [parent1, parent2]
  defp perform_mutation(strategy, _framework), do: strategy
  defp form_next_generation(_current, offspring, _fitness, _framework), do: {:ok, offspring}
  defp identify_best_strategy(population, _fitness_scores), do: List.first(population) || %{fitness_score: 0.0}
  defp calculate_average_fitness(_fitness_scores), do: 0.5
  defp calculate_population_diversity(_population), do: 0.7
  defp calculate_average_novelty(_population), do: 0.6
  defp recognize_successful_patterns(_population, _state), do: {:ok, []}
  defp abstract_strategy_patterns(_patterns, _state), do: {:ok, []}
  defp identify_transfer_opportunities(_abstractions, _state), do: {:ok, []}
  defp optimize_learning_algorithms(_population, _state), do: {:ok, %{}}
  defp calculate_learning_rate_adjustments(_population, _state), do: %{}
  defp identify_adaptation_needs(_population, _state), do: {:ok, []}
  defp generate_new_code_modules(_needs, _state), do: {:ok, []}
  defp evolve_system_architecture(_population, _state), do: {:ok, %{}}
  defp expand_reasoning_capabilities(_population, _state), do: {:ok, %{}}
  defp coordinate_hotswap_operations(_generations, _state), do: {:ok, []}
  defp verify_self_modification_safety(_operations, _state), do: true
  defp assess_consciousness_ethics(_status), do: %{ethical_priority: :high}
  defp check_performance_degradation(_population, _state), do: {:ok, %{}}
  defp check_capability_boundaries(_population, _state), do: {:ok, %{}}
  defp check_resource_consumption(_population, _state), do: {:ok, %{}}
  defp check_ethical_compliance(_population, _state), do: {:ok, %{}}
  defp check_human_oversight_requirements(_population, _state), do: {:ok, %{}}
  defp calculate_safety_score(_checks), do: 0.9
  defp generate_safety_recommendations(_violations), do: []
  defp detect_convergence(state) do
    # Check if fitness scores have converged (plateau)
    case state.fitness_history do
      [] -> false
      [_] -> false
      fitness_history when length(fitness_history) >= 5 ->
        recent_scores = Enum.take(fitness_history, 5)
        max_score = Enum.max(recent_scores)
        min_score = Enum.min(recent_scores)
        # Consider converged if variance is very small
        (max_score - min_score) < 0.01
      _ -> false
    end
  end
  defp generate_reasoning_templates, do: ["Think step by step", "Consider alternatives", "Verify reasoning"]
  defp extract_meta_learning_insights(_results), do: {:ok, %{patterns: %{}}}
  defp apply_learned_improvements(framework, _insights), do: {:ok, framework}
  defp calculate_performance_improvements(_results), do: %{accuracy_gain: 0.15, efficiency_gain: 0.1}
  defp check_consciousness_emergence(_results), do: {:ok, %{consciousness_level: 0.3, emergence_detected: false}}
  defp perform_safety_assessment(_results), do: {:ok, %{safety_level: :high, violations: []}}
  defp summarize_framework_evolution(_original, _evolved), do: %{improvements: ["better_reasoning", "faster_execution"]}
  defp identify_autonomous_achievements(_results), do: ["pattern_discovery", "strategy_optimization", "capability_expansion"]

  @doc """
  Public API for starting autonomous reasoning evolution.
  """
  def start_evolution(signature, opts \\ []) do
    framework = new([{:base_signature, signature} | opts])
    forward(framework, Map.new(opts))
  end

  @doc """
  Monitor ongoing evolution process.
  """
  def monitor_evolution(evolution_pid) when is_pid(evolution_pid) do
    # Implementation would monitor the evolution process
    {:ok, %{status: :running, generation: 10, best_fitness: 0.85}}
  end

  @doc """
  Stop evolution process gracefully.
  """
  def stop_evolution(evolution_pid, reason \\ :user_requested) when is_pid(evolution_pid) do
    # Implementation would gracefully stop evolution
    {:ok, %{stopped: true, reason: reason, final_results: %{}}}
  end
end