Phase 5: Recursive Meta-Cognition & Evolutionary Optimization

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Phase 5: Recursive Meta-Cognition & Evolutionary Optimization\n\n## Objective\nEnable the system to observe, analyze, and recursively improve itself using evolutionary algorithms.\n\n## Implementation Steps\n- [ ] Implement feedback-driven self-analysis modules\n- [ ] Create introspection mechanisms for cognitive processes\n- [ ] Design recursive cognitive improvement algorithms\n- [ ] Integrate MOSES (or equivalent) for kernel evolution\n- [ ] Implement cognitive performance monitoring\n- [ ] Continuous benchmarking system for cognitive components\n- [ ] Self-tuning of kernels and agents based on performance\n- [ ] Document evolutionary trajectories and fitness landscapes\n- [ ] Implement genetic algorithms for cognitive architecture evolution\n\n## Meta-Cognitive Components\n### Self-Analysis Modules\n- Performance Profiler: Continuous cognitive performance monitoring\n- Pattern Analyzer: Detection of cognitive behavioral patterns\n- Efficiency Assessor: Identification of optimization opportunities\n- Stability Monitor: Detection of cognitive instabilities\n\n### Evolutionary Mechanisms\n- Genetic Operators: Crossover, mutation, selection for cognitive architectures\n- Fitness Functions: Multi-objective optimization criteria\n- Population Management: Diverse cognitive architecture populations\n- Elitism Strategies: Preservation of high-performing configurations\n\n## Success Criteria\n- ✅ System demonstrates measurable self-improvement over time\n- ✅ Meta-cognitive processes operate without infinite recursion\n- ✅ Evolutionary optimization improves cognitive efficiency\n- ✅ Self-analysis produces actionable insights\n- ✅ System maintains stability during self-modification\n\nPart of the Distributed Agentic Cognitive Grammar Network development cycle.