Phase 36 Planning — Evolutionary & Neuroevolution Algorithms

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Phase 36 — Evolutionary \u0026 Neuroevolution Algorithms\n\n### Overview\n\nPhase 36 introduces Evolutionary \u0026 Neuroevolution Algorithms to ASI-Build, providing population-based optimization, genetic operators, multi-objective fitness evaluation, and neural architecture search through neuroevolution. Building on Phase 35's quantum-classical hybrid computing, evolutionary methods offer a complementary optimization paradigm that excels in vast, non-differentiable search spaces.\n\n### Sub-Phase Roadmap\n\n| Sub-Phase | Component | Description |\n|-----------|-----------|-------------|\n| 36.1 | GeneticOperator | Selection (tournament, roulette, rank), crossover (uniform, single-point, BLX-α), mutation (Gaussian, polynomial, adaptive), elitism strategies |\n| 36.2 | FitnessEvaluator | Multi-objective fitness landscapes, Pareto front computation (NSGA-II, MOEA/D), fitness sharing, novelty search metrics |\n| 36.3 | NeuroevolutionEngine | NEAT, HyperNEAT, weight evolution, topology search, complexification |\n| 36.4 | PopulationManager | Speciation \u0026 niching, diversity maintenance (crowding distance, fitness sharing), island model migration, population sizing |\n| 36.5 | EvolutionaryOrchestrator | Unified evolutionary pipeline, generation lifecycle, convergence detection, adaptive parameter control |\n\n### Architecture Overview\n\n\n┌─────────────────────────────────────────────────────────┐\n│ EvolutionaryOrchestrator │\n│ ┌──────────────┐ ┌──────────────┐ ┌───────────────┐ │\n│ │GeneticOperator│ │FitnessEvalua-│ │Neuroevolution │ │\n│ │ - Selection │ │ tor │ │ Engine │ │\n│ │ - Crossover │ │ - Pareto │ │ - NEAT │ │\n│ │ - Mutation │ │ - NSGA-II │ │ - HyperNEAT │ │\n│ │ - Elitism │ │ - Novelty │ │ - Topology │ │\n│ └──────┬───────┘ └──────┬───────┘ └──────┬────────┘ │\n│ │ │ │ │\n│ ┌──────┴─────────────────┴──────────────────┴────────┐ │\n│ │ PopulationManager │ │\n│ │ Speciation · Niching · Island Migration · Sizing │ │\n│ └─────────────────────────────────────────────────────┘ │\n└─────────────────────────────────────────────────────────┘\n ↕ ↕ ↕\n Phase 35 Quantum Phase 14 Code Phase 20 Reasoning\n Variational Opt. Synthesis Inference\n\n\n### Relationship to Phase 35\n\nPhase 35's variational quantum algorithms (VQE, QAOA) optimize parameterized circuits via gradient-based methods. Phase 36's evolutionary algorithms provide gradient-free alternatives that can:\n- Optimize quantum circuit topologies (architecture search)\n- Tune variational parameters in barren-plateau landscapes\n- Discover novel ansatz structures via neuroevolution\n- Handle discrete/combinatorial quantum gate selection\n\n### Academic References\n\n1. Holland (1975) — Adaptation in Natural and Artificial Systems — foundational genetic algorithm theory, schema theorem\n2. Goldberg (1989) — Genetic Algorithms in Search, Optimization, and Machine Learning — canonical GA operators, building blocks\n3. Deb et al. (2002) — A fast and elitist multiobjective genetic algorithm: NSGA-II — fast non-dominated sorting, crowding distance\n4. Stanley \u0026 Miikkulainen (2002) — Evolving neural networks through augmenting topologies (NEAT) — topology \u0026 weight co-evolution\n5. Stanley et al. (2009) — A hypercube-based encoding for evolving large-scale neural networks (HyperNEAT) — compositional pattern-producing networks\n6. Lehman \u0026 Stanley (2011) — Abandoning objectives: Evolution through the search for novelty alone — novelty search\n7. Such et al. (2017) — Deep neuroevolution: Genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning — large-scale neuroevolution (Uber AI)\n8. Salimans et al. (2017) — Evolution strategies as a scalable alternative to reinforcement learning — OpenAI ES\n9. Real et al. (2019) — Regularized evolution for image classifier architecture search — AmoebaNet, aging regularization\n10. Eiben \u0026 Smith (2015) — Introduction to Evolutionary Computing — comprehensive EC textbook, operator taxonomy\n\n### Implementation Notes\n\n- All components use @dataclass(frozen=True) for immutable configuration\n- Async-first design with asyncio throughout\n- Integration with Phase 14 CodeSynthesiser for evolved program synthesis\n- Integration with Phase 35 QuantumCircuitBuilder for quantum architecture search\n- Comprehensive type hints and Protocol-based interfaces