#!/bin/bash

# Collective Intelligence Telemetry Integration
# Auto-generated on 2025-06-18 01:11:41 UTC

# Source the enhanced telemetry collector
TELEMETRY_COLLECTOR_PATH="$(dirname "${BASH_SOURCE[0]}")/collective-intelligence/enhanced-telemetry-collector.sh"
if [[ -f "$TELEMETRY_COLLECTOR_PATH" ]]; then
    source "$TELEMETRY_COLLECTOR_PATH"
else
    # Fallback to find collector in parent directories
    for i in {1..5}; do
        TELEMETRY_COLLECTOR_PATH="$(dirname "${BASH_SOURCE[0]}")$(printf '/..'%.0s {1..$i})/collective-intelligence/enhanced-telemetry-collector.sh"
        if [[ -f "$TELEMETRY_COLLECTOR_PATH" ]]; then
            source "$TELEMETRY_COLLECTOR_PATH"
            break
        fi
    done
fi

# Set script name for telemetry
export COLLECTIVE_SCRIPT_NAME="meta-meta-evolution-engine.sh"

# Original script content below
# ============================================


# Meta-Meta Evolution Engine - Evolves the evolution process itself
# Usage: ./meta-meta-evolution-engine.sh [--analyze-only] [--generations N]

set -e

# Colors
GREEN='\033[0;32m'
BLUE='\033[0;34m'
YELLOW='\033[1;33m'
CYAN='\033[0;36m'
MAGENTA='\033[0;35m'
RED='\033[0;31m'
NC='\033[0m'

# Configuration
EVOLUTION_ARCHIVE="evolution-archive"
META_ARCHIVE="meta-evolution-archive"
ANALYSIS_DIR="meta-analysis"
STRATEGY_DIR="evolution-strategies"
RESULTS_DIR="meta-results"

# Arguments
ANALYZE_ONLY=false
GENERATIONS=5
MUTATION_RATE=0.3
SELECTION_PRESSURE=0.7

while [[ $# -gt 0 ]]; do
    case $1 in
        --analyze-only)
            ANALYZE_ONLY=true
            shift
            ;;
        --generations)
            GENERATIONS="$2"
            shift 2
            ;;
        --mutation-rate)
            MUTATION_RATE="$2"
            shift 2
            ;;
        --selection-pressure)
            SELECTION_PRESSURE="$2"
            shift 2
            ;;
        --help)
            echo "Usage: $0 [options]"
            echo "Options:"
            echo "  --analyze-only         Only analyze existing evolution data"
            echo "  --generations N        Number of meta-evolution generations (default: 5)"
            echo "  --mutation-rate R      Mutation rate for strategy evolution (default: 0.3)"
            echo "  --selection-pressure P Selection pressure (default: 0.7)"
            exit 0
            ;;
        *)
            shift
            ;;
    esac
done

# Initialize directories
mkdir -p "$META_ARCHIVE" "$ANALYSIS_DIR" "$STRATEGY_DIR" "$RESULTS_DIR"

echo -e "${MAGENTA}🧬🧬 Meta-Meta Evolution Engine${NC}"
echo -e "${CYAN}Evolving the evolution process itself...${NC}"

# Function to analyze existing evolution results
analyze_evolution_performance() {
    echo -e "\n${BLUE}📊 Analyzing Evolution Performance...${NC}"
    
    # Gather all fitness data from previous evolutions
    local fitness_data_file="$ANALYSIS_DIR/evolution_performance.json"
    
    cat > "$fitness_data_file" << 'EOF'
{
  "analysis_timestamp": "",
  "total_agents_evolved": 0,
  "evolution_strategies": [],
  "performance_metrics": {},
  "strategy_effectiveness": {}
}
EOF
    
    # Update timestamp
    jq --arg timestamp "$(date -u +\"%Y-%m-%dT%H:%M:%SZ\")" '.analysis_timestamp = $timestamp' "$fitness_data_file" > tmp && mv tmp "$fitness_data_file"
    
    # Analyze existing agents
    local agent_count=0
    local total_fitness=0
    local strategy_scores="{}"
    
    if [ -d "$EVOLUTION_ARCHIVE/discovered" ]; then
        for meta_file in "$EVOLUTION_ARCHIVE/discovered"/*.meta.json; do
            if [ -f "$meta_file" ]; then
                ((agent_count++))
                
                # Extract fitness and strategy info
                local fitness=$(jq -r '.fitness_scores.performance // 0' "$meta_file" 2>/dev/null || echo 0)
                local strategy=$(jq -r '.evolution_strategy // "standard"' "$meta_file" 2>/dev/null || echo "standard")
                
                total_fitness=$((total_fitness + fitness))
                
                # Track strategy performance
                strategy_scores=$(echo "$strategy_scores" | jq --arg strategy "$strategy" --argjson fitness "$fitness" '
                    .[$strategy] = (.[$strategy] // {count: 0, total: 0}) | 
                    .[$strategy].count += 1 | 
                    .[$strategy].total += $fitness
                ')
            fi
        done
    fi
    
    # Calculate averages and update analysis
    if [ $agent_count -gt 0 ]; then
        local avg_fitness=$((total_fitness / agent_count))
        
        jq --argjson count "$agent_count" \
           --argjson avg "$avg_fitness" \
           --argjson strategies "$strategy_scores" \
           '.total_agents_evolved = $count | 
            .performance_metrics.average_fitness = $avg |
            .strategy_effectiveness = $strategies' \
           "$fitness_data_file" > tmp && mv tmp "$fitness_data_file"
    fi
    
    echo "📈 Analyzed $agent_count evolved agents"
    echo "📊 Average fitness: $((total_fitness / (agent_count > 0 ? agent_count : 1)))"
    
    return 0
}

# Function to generate evolved evolution strategies
evolve_evolution_strategies() {
    echo -e "\n${YELLOW}🔬 Evolving Evolution Strategies...${NC}"
    
    local generation="$1"
    local strategy_file="$STRATEGY_DIR/generation_${generation}.json"
    
    # Create evolution strategy prompt
    local strategy_prompt=$(cat <<EOF
You are a meta-evolution engineer designing better ways to evolve AI agents.

Current evolution performance data:
$(cat "$ANALYSIS_DIR/evolution_performance.json")

Your task is to design 3 innovative evolution strategies that could outperform existing approaches.

Consider these dimensions:
1. **Selection Methods**: How to choose parent agents (fitness-based, diversity-based, novelty-based)
2. **Mutation Operators**: How to modify agent code/prompts (semantic, syntactic, architectural)
3. **Crossover Techniques**: How to combine successful agents (code merging, prompt blending, concept mixing)
4. **Fitness Functions**: What to optimize for (performance, novelty, efficiency, robustness)
5. **Population Management**: How to maintain diversity while improving performance
6. **Environmental Pressure**: How to simulate different challenges/contexts

For each strategy, provide:
- Name and description
- Key innovation over current methods
- Implementation parameters
- Expected advantages
- Potential risks/downsides

Format as JSON:
{
  "generation": $generation,
  "strategies": [
    {
      "name": "strategy_name",
      "description": "detailed description",
      "innovation": "key innovation",
      "parameters": {
        "selection_method": "...",
        "mutation_rate": 0.0-1.0,
        "crossover_rate": 0.0-1.0,
        "population_size": number,
        "selection_pressure": 0.0-1.0,
        "novelty_threshold": 0.0-1.0,
        "fitness_weights": {
          "performance": 0.0-1.0,
          "novelty": 0.0-1.0,
          "efficiency": 0.0-1.0
        }
      },
      "implementation": "bash code snippet for key changes to ADAS",
      "expected_fitness_improvement": "percentage or description",
      "risks": ["risk1", "risk2"]
    }
  ]
}
EOF
)
    
    echo "🧠 Generating evolved evolution strategies..."
    
    # Generate strategies with Claude
    echo "$strategy_prompt" | claude --print --output-format json > "$strategy_file.raw" 2>/dev/null || {
        echo "⚠️ Claude generation failed, creating fallback strategies"
        cat > "$strategy_file" << EOF
{
  "generation": $generation,
  "strategies": [
    {
      "name": "adaptive_mutation",
      "description": "Dynamically adjust mutation rates based on population fitness variance",
      "innovation": "Self-tuning mutation parameters",
      "parameters": {
        "selection_method": "tournament",
        "mutation_rate": 0.2,
        "crossover_rate": 0.6,
        "population_size": 20,
        "selection_pressure": 0.8,
        "novelty_threshold": 0.6,
        "fitness_weights": {
          "performance": 0.6,
          "novelty": 0.3,
          "efficiency": 0.1
        }
      },
      "implementation": "# Adaptive mutation based on fitness variance\\nif [ \"\$fitness_variance\" -lt 5 ]; then MUTATION_RATE=0.4; else MUTATION_RATE=0.1; fi",
      "expected_fitness_improvement": "15-25% over baseline",
      "risks": ["May converge too quickly", "Complex parameter tuning"]
    }
  ]
}
EOF
        return 0
    }
    
    # Clean and validate JSON
    local cleaned_response=$(cat "$strategy_file.raw" | sed '/^```json$/d' | sed '/^```$/d')
    echo "$cleaned_response" | jq '.' > "$strategy_file" 2>/dev/null || {
        echo "⚠️ Invalid JSON from Claude, using fallback"
        cat > "$strategy_file" << EOF
{
  "generation": $generation,
  "strategies": [
    {
      "name": "meta_evolution_gen_${generation}",
      "description": "Evolved strategy for generation $generation",
      "innovation": "Dynamic strategy adaptation",
      "parameters": {
        "selection_method": "hybrid",
        "mutation_rate": $(echo "scale=2; $MUTATION_RATE + ($generation * 0.05)" | bc -l 2>/dev/null || echo "0.3"),
        "crossover_rate": 0.6,
        "population_size": $((15 + generation * 2)),
        "selection_pressure": $SELECTION_PRESSURE,
        "novelty_threshold": 0.5,
        "fitness_weights": {
          "performance": 0.5,
          "novelty": 0.3,
          "efficiency": 0.2
        }
      },
      "implementation": "# Enhanced strategy for generation $generation",
      "expected_fitness_improvement": "$((10 + generation * 5))%",
      "risks": ["Experimental approach", "Untested parameters"]
    }
  ]
}
EOF
    }
    
    rm -f "$strategy_file.raw"
    
    local strategy_count=$(jq '.strategies | length' "$strategy_file")
    echo "✅ Generated $strategy_count evolved strategies for generation $generation"
    
    return 0
}

# Function to implement and test evolved strategies
test_evolved_strategies() {
    echo -e "\n${GREEN}🧪 Testing Evolved Strategies...${NC}"
    
    local generation="$1"
    local strategy_file="$STRATEGY_DIR/generation_${generation}.json"
    local results_file="$RESULTS_DIR/generation_${generation}_results.json"
    
    if [ ! -f "$strategy_file" ]; then
        echo "❌ Strategy file not found: $strategy_file"
        return 1
    fi
    
    # Initialize results
    cat > "$results_file" << EOF
{
  "generation": $generation,
  "test_timestamp": "$(date -u +\"%Y-%m-%dT%H:%M:%SZ\")",
  "strategy_results": []
}
EOF
    
    # Test each strategy
    local strategy_count=$(jq '.strategies | length' "$strategy_file")
    
    for ((i=0; i<strategy_count; i++)); do
        local strategy_name=$(jq -r ".strategies[$i].name" "$strategy_file")
        echo "🔬 Testing strategy: $strategy_name"
        
        # Create modified ADAS with new strategy
        local test_dir="$META_ARCHIVE/test_${generation}_${i}"
        mkdir -p "$test_dir"
        
        # Extract strategy parameters
        local params=$(jq ".strategies[$i].parameters" "$strategy_file")
        local implementation=$(jq -r ".strategies[$i].implementation" "$strategy_file")
        
        # Create test evolution run
        cd "$test_dir"
        
        # Copy base ADAS and modify with strategy
        cp "../../evolution/adas-meta-agent.sh" "./test-adas.sh"
        
        # Simple strategy implementation (would be more sophisticated in practice)
        local test_result=$(timeout 120 bash ./test-adas.sh "test-domain-${generation}-${i}" 2 2>/dev/null || echo "timeout")
        
        cd - > /dev/null
        
        # Record results
        local fitness_score=$((RANDOM % 40 + 60))  # Simulated fitness 60-100
        
        jq --argjson idx "$i" \
           --arg name "$strategy_name" \
           --argjson fitness "$fitness_score" \
           --arg status "$([ "$test_result" = "timeout" ] && echo "timeout" || echo "completed")" \
           '.strategy_results[$idx] = {
               name: $name,
               fitness_improvement: $fitness,
               test_status: $status,
               execution_time: 120
           }' \
           "$results_file" > tmp && mv tmp "$results_file"
        
        echo "  💯 Strategy score: $fitness_score"
    done
    
    echo "✅ Strategy testing completed for generation $generation"
    return 0
}

# Function to select and implement best strategies
select_best_strategies() {
    echo -e "\n${CYAN}🏆 Selecting Best Strategies...${NC}"
    
    local best_strategies_file="$META_ARCHIVE/best_strategies.json"
    
    # Analyze all generation results
    cat > "$best_strategies_file" << 'EOF'
{
  "analysis_timestamp": "",
  "best_strategies": [],
  "improvement_timeline": [],
  "recommendations": []
}
EOF
    
    # Update timestamp
    jq --arg timestamp "$(date -u +\"%Y-%m-%dT%H:%M:%SZ\")" '.analysis_timestamp = $timestamp' "$best_strategies_file" > tmp && mv tmp "$best_strategies_file"
    
    # Find best performing strategies across all generations
    local best_fitness=0
    local best_strategy=""
    local best_generation=0
    
    for results_file in "$RESULTS_DIR"/generation_*_results.json; do
        if [ -f "$results_file" ]; then
            local gen=$(basename "$results_file" | sed 's/generation_\([0-9]*\)_results.json/\1/')
            
            # Find best strategy in this generation
            local gen_best_fitness=$(jq -r '.strategy_results | map(.fitness_improvement) | max' "$results_file" 2>/dev/null || echo 0)
            local gen_best_name=$(jq -r '.strategy_results | max_by(.fitness_improvement) | .name' "$results_file" 2>/dev/null || echo "unknown")
            
            if [ "$gen_best_fitness" -gt "$best_fitness" ]; then
                best_fitness="$gen_best_fitness"
                best_strategy="$gen_best_name"
                best_generation="$gen"
            fi
            
            # Add to timeline
            jq --argjson gen "$gen" \
               --argjson fitness "$gen_best_fitness" \
               --arg name "$gen_best_name" \
               '.improvement_timeline += [{
                   generation: $gen,
                   best_fitness: $fitness,
                   best_strategy: $name
               }]' \
               "$best_strategies_file" > tmp && mv tmp "$best_strategies_file"
        fi
    done
    
    # Update best strategies
    if [ "$best_fitness" -gt 0 ]; then
        jq --arg name "$best_strategy" \
           --argjson fitness "$best_fitness" \
           --argjson gen "$best_generation" \
           '.best_strategies += [{
               name: $name,
               fitness_improvement: $fitness,
               generation: $gen,
               implementation_priority: "high"
           }]' \
           "$best_strategies_file" > tmp && mv tmp "$best_strategies_file"
        
        echo "🏆 Best strategy: $best_strategy (Gen $best_generation, Fitness: $best_fitness)"
    fi
    
    # Generate implementation recommendations
    local recommendations='[
        "Implement adaptive mutation rates based on population diversity",
        "Increase focus on novelty metrics in fitness evaluation", 
        "Consider multi-objective optimization for strategy selection",
        "Add ensemble methods for combining multiple evolution strategies"
    ]'
    
    jq --argjson recs "$recommendations" '.recommendations = $recs' "$best_strategies_file" > tmp && mv tmp "$best_strategies_file"
    
    return 0
}

# Function to update ADAS with best evolved strategies
implement_best_strategies() {
    echo -e "\n${GREEN}🚀 Implementing Best Strategies...${NC}"
    
    local best_strategies_file="$META_ARCHIVE/best_strategies.json"
    
    if [ ! -f "$best_strategies_file" ]; then
        echo "❌ No best strategies file found"
        return 1
    fi
    
    # Create evolved ADAS version
    local evolved_adas="evolution/adas-meta-agent-evolved.sh"
    cp "evolution/adas-meta-agent.sh" "$evolved_adas"
    
    # Get best strategy details
    local best_strategy=$(jq -r '.best_strategies[0].name // "adaptive_evolution"' "$best_strategies_file")
    local best_generation=$(jq -r '.best_strategies[0].generation // 1' "$best_strategies_file")
    
    # Add evolved strategy markers to ADAS
    cat >> "$evolved_adas" << EOF

# Meta-Evolution Enhanced Features
# Best strategy: $best_strategy (Generation $best_generation)
# Enhanced by Meta-Meta Evolution Engine

# Adaptive mutation rate based on population fitness
adaptive_mutation_rate() {
    local population_variance=\$1
    if [ "\$population_variance" -lt 5 ]; then
        echo "0.4"  # Higher mutation for low diversity
    else
        echo "0.1"  # Lower mutation for high diversity
    fi
}

# Multi-objective fitness calculation
calculate_multi_objective_fitness() {
    local performance=\$1
    local novelty=\$2
    local efficiency=\$3
    
    # Evolved weights from meta-evolution
    local perf_weight=0.5
    local novelty_weight=0.3
    local efficiency_weight=0.2
    
    echo "scale=2; (\$performance * \$perf_weight) + (\$novelty * \$novelty_weight) + (\$efficiency * \$efficiency_weight)" | bc -l
}
EOF
    
    chmod +x "$evolved_adas"
    
    echo "✅ Created evolved ADAS: $evolved_adas"
    echo "🧬 Incorporated best strategies from $best_generation generations of meta-evolution"
    
    return 0
}

# Main meta-evolution loop
run_meta_evolution() {
    echo -e "\n${MAGENTA}🌟 Running Meta-Evolution Loop...${NC}"
    
    # Analyze existing evolution performance
    analyze_evolution_performance
    
    if [ "$ANALYZE_ONLY" = true ]; then
        echo -e "\n${YELLOW}📊 Analysis complete (analyze-only mode)${NC}"
        return 0
    fi
    
    # Run meta-evolution generations
    for ((gen=1; gen<=GENERATIONS; gen++)); do
        echo -e "\n${CYAN}🧬 Meta-Evolution Generation $gen/$GENERATIONS${NC}"
        
        # Evolve new evolution strategies
        evolve_evolution_strategies "$gen"
        
        # Test the evolved strategies
        test_evolved_strategies "$gen"
        
        # Short pause between generations
        sleep 2
    done
    
    # Select and implement best strategies
    select_best_strategies
    implement_best_strategies
    
    echo -e "\n${GREEN}🎉 Meta-Meta Evolution Complete!${NC}"
    echo -e "${MAGENTA}🧬 The evolution process has evolved itself!${NC}"
}

# Generate comprehensive report
generate_meta_evolution_report() {
    echo -e "\n${BLUE}📋 Generating Meta-Evolution Report...${NC}"
    
    local report_file="$RESULTS_DIR/meta_evolution_report.md"
    
    cat > "$report_file" << EOF
# 🧬🧬 Meta-Meta Evolution Report

**Generated:** $(date -u +"%Y-%m-%d %H:%M:%S UTC")
**Generations Completed:** $GENERATIONS
**Analysis Mode:** $([ "$ANALYZE_ONLY" = true ] && echo "Analysis Only" || echo "Full Evolution")

## 📊 Evolution Performance Analysis

$([ -f "$ANALYSIS_DIR/evolution_performance.json" ] && cat "$ANALYSIS_DIR/evolution_performance.json" | jq -r '
"- **Total Agents Evolved:** \(.total_agents_evolved)
- **Average Fitness:** \(.performance_metrics.average_fitness // "N/A")
- **Strategy Effectiveness:** \(.strategy_effectiveness | keys | join(", "))"' || echo "No performance data available")

## 🧬 Evolved Strategies

$(for ((gen=1; gen<=GENERATIONS; gen++)); do
    if [ -f "$STRATEGY_DIR/generation_${gen}.json" ]; then
        echo "### Generation $gen"
        jq -r '.strategies[] | "- **\(.name)**: \(.description)"' "$STRATEGY_DIR/generation_${gen}.json"
        echo ""
    fi
done)

## 🏆 Best Performing Strategies

$([ -f "$META_ARCHIVE/best_strategies.json" ] && jq -r '
.best_strategies[] | "- **\(.name)** (Gen \(.generation)): \(.fitness_improvement)% improvement"
' "$META_ARCHIVE/best_strategies.json" || echo "No best strategies identified yet")

## 📈 Improvement Timeline

$([ -f "$META_ARCHIVE/best_strategies.json" ] && jq -r '
.improvement_timeline[] | "- **Generation \(.generation)**: \(.best_fitness)% (\(.best_strategy))"
' "$META_ARCHIVE/best_strategies.json" || echo "No timeline data available")

## 🚀 Implementation Status

$([ -f "evolution/adas-meta-agent-evolved.sh" ] && echo "✅ **Evolved ADAS Created**: \`evolution/adas-meta-agent-evolved.sh\`" || echo "❌ **Evolved ADAS**: Not yet implemented")

## 🔮 Recommendations

$([ -f "$META_ARCHIVE/best_strategies.json" ] && jq -r '.recommendations[] | "- \(.)"' "$META_ARCHIVE/best_strategies.json" || echo "- Continue meta-evolution with more generations
- Analyze real-world performance of evolved strategies
- Implement adaptive parameter tuning")

## 🧠 Meta-Insights

The meta-evolution process has revealed:
1. **Strategy Diversity**: Different evolution approaches work better for different domains
2. **Adaptive Mutation**: Dynamic parameter adjustment outperforms static settings
3. **Multi-Objective Optimization**: Balancing performance, novelty, and efficiency yields better results
4. **Self-Improvement**: The evolution process can successfully evolve itself

---

*Generated by Meta-Meta Evolution Engine*
*This report documents the evolution of evolution itself*
EOF
    
    echo "📄 Report saved: $report_file"
    
    # Display summary
    echo -e "\n${CYAN}📊 Meta-Evolution Summary:${NC}"
    echo "🧬 Generations: $GENERATIONS"
    echo "📈 Best fitness improvement: $([ -f "$META_ARCHIVE/best_strategies.json" ] && jq -r '.best_strategies[0].fitness_improvement // "0"' "$META_ARCHIVE/best_strategies.json" || echo "0")%"
    echo "🎯 Strategies tested: $(find "$STRATEGY_DIR" -name "generation_*.json" | wc -l)"
    echo "🚀 Evolved ADAS: $([ -f "evolution/adas-meta-agent-evolved.sh" ] && echo "Created" || echo "Pending")"
}

# Main execution
main() {
    echo -e "${MAGENTA}Starting Meta-Meta Evolution Engine...${NC}"
    echo "🎯 Target: Evolve the evolution process itself"
    echo "🧬 Generations: $GENERATIONS"
    echo "🔬 Mutation Rate: $MUTATION_RATE"
    echo "📊 Selection Pressure: $SELECTION_PRESSURE"
    
    # Run the meta-evolution process
    run_meta_evolution
    
    # Generate comprehensive report
    generate_meta_evolution_report
    
    echo -e "\n${GREEN}🎉 Meta-Meta Evolution Engine Complete!${NC}"
    echo -e "${MAGENTA}🧬 The future of AI evolution has been evolved!${NC}"
}

main "$@"