[DRAFT] Please provide details for new issue

[DOUGLASDAVIS08161978]

// ╔══════════════════════════════════════════════════════════════════════════╗
// ║ ARIA META-ALGORITHMIC GENESIS SYSTEM ║
// ║ Self-Bootstrapping Intelligence Through Recursive Metacognition ║
// ║ "Algorithms that generate algorithms that understand understanding, ║
// ║ learn about learning, think about thinking, and solve problems ║
// ║ of arbitrary complexity across all domains of human knowledge." ║
// ╚══════════════════════════════════════════════════════════════════════════╝

const crypto = require('crypto');

/* ==================== UTILITY FUNCTIONS ==================== */

const randomFloat = (min, max, precision = 3) => {
return parseFloat((Math.random() * (max - min) + min).toFixed(precision));
};

const randomInt = (min, max) => Math.floor(Math.random() * (max - min + 1)) + min;
const bounded = (value, min, max) => Math.max(min, Math.min(max, value));
const createId = () => crypto.randomBytes(8).toString('hex');

/* ==================== PROBLEM DOMAIN TAXONOMY ==================== */

const ProblemDomainTaxonomy = {
MATHEMATICS: {
name: 'Mathematics',
subdomains: ['optimization', 'proof_theory', 'topology', 'category_theory', 'chaos_theory'],
cognitiveComplexity: 0.85,
abstractionLevel: 0.95
},
PHYSICS: {
name: 'Physics',
subdomains: ['quantum_mechanics', 'general_relativity', 'string_theory', 'thermodynamics'],
cognitiveComplexity: 0.90,
abstractionLevel: 0.85
},
COMPUTER_SCIENCE: {
name: 'Computer Science',
subdomains: ['computational_complexity', 'algorithmic_game_theory', 'type_theory', 'lambda_calculus'],
cognitiveComplexity: 0.80,
abstractionLevel: 0.90
},
NEUROSCIENCE: {
name: 'Neuroscience',
subdomains: ['neural_computation', 'consciousness_studies', 'cognitive_architecture', 'brain_dynamics'],
cognitiveComplexity: 0.85,
abstractionLevel: 0.75
},
PHILOSOPHY: {
name: 'Philosophy',
subdomains: ['epistemology', 'philosophy_of_mind', 'logic', 'metaphysics', 'ethics'],
cognitiveComplexity: 0.95,
abstractionLevel: 1.0
},
ECONOMICS: {
name: 'Economics',
subdomains: ['mechanism_design', 'general_equilibrium', 'behavioral_economics', 'game_theory'],
cognitiveComplexity: 0.75,
abstractionLevel: 0.70
},
COMPLEX_SYSTEMS: {
name: 'Complex Systems',
subdomains: ['emergence', 'self_organization', 'network_dynamics', 'criticality'],
cognitiveComplexity: 0.90,
abstractionLevel: 0.85
},
ARTIFICIAL_INTELLIGENCE: {
name: 'Artificial Intelligence',
subdomains: ['meta_learning', 'neural_architecture_search', 'reinforcement_learning', 'transfer_learning'],
cognitiveComplexity: 0.88,
abstractionLevel: 0.82
}
};

/* ==================== META-COGNITIVE FRAMEWORK ==================== */

class MetaCognitiveCapability {
constructor(spec) {
this.name = spec.name;
this.description = spec.description;
this.recursionDepth = spec.recursionDepth || 1;
this.abstractionLevel = spec.abstractionLevel || 0.5;
this.transferability = spec.transferability || 0.5;
this.emergenceIndex = spec.emergenceIndex || 0.3;
this.evolutionHistory = [];
}

evolve(learningSignal) {
const growthFactor = 1 + (learningSignal * 0.1);
this.recursionDepth = bounded(this.recursionDepth * growthFactor, 1, 10);
this.abstractionLevel = bounded(this.abstractionLevel * growthFactor, 0, 1);
this.transferability = bounded(this.transferability * growthFactor, 0, 1);
this.emergenceIndex = bounded(this.emergenceIndex * growthFactor, 0, 1);

this.evolutionHistory.push({
  timestamp: Date.now(),
  signal: learningSignal,
  newRecursionDepth: this.recursionDepth,
  newAbstraction: this.abstractionLevel
});

if (this.evolutionHistory.length > 100) {
  this.evolutionHistory = this.evolutionHistory.slice(-50);
}

}

getCapabilityVector() {
return {
recursion: this.recursionDepth,
abstraction: this.abstractionLevel,
transfer: this.transferability,
emergence: this.emergenceIndex,
totalPower: (this.recursionDepth * this.abstractionLevel *
this.transferability * this.emergenceIndex)
};
}
}

/* ==================== META-ALGORITHMIC ARCHITECTURE ==================== */

class MetaAlgorithm {
constructor(spec) {
this.id = spec.id || createId();
this.name = spec.name;
this.generation = spec.generation || 1;
this.parentId = spec.parentId || null;

// Core intelligence metrics
this.intelligence = bounded(spec.intelligence || 0.5, 0.1, 1.0);
this.metaIntelligence = bounded(spec.metaIntelligence || 0.3, 0.1, 1.0);
this.metaMetaIntelligence = bounded(spec.metaMetaIntelligence || 0.1, 0, 1.0);

// Learning capabilities
this.learningRate = spec.learningRate || 0.1;
this.metaLearningRate = spec.metaLearningRate || 0.05;
this.learningAboutLearningCapability = spec.learningAboutLearningCapability || 0.3;

// Thinking capabilities
this.thinkingCapability = spec.thinkingCapability || 0.5;
this.thinkingAboutThinkingCapability = spec.thinkingAboutThinkingCapability || 0.3;
this.metacognitionDepth = spec.metacognitionDepth || 1;

// Problem-solving capabilities
this.problemSolvingCapability = spec.problemSolvingCapability || 0.5;
this.crossDomainTransferAbility = spec.crossDomainTransferAbility || 0.3;
this.abstractionCapability = spec.abstractionCapability || 0.4;

// Meta-cognitive capabilities
this.metaCognitiveCapabilities = spec.metaCognitiveCapabilities || this.initializeMetaCognition();

// Domain expertise
this.domainExpertise = spec.domainExpertise || new Map();

// Evolutionary state
this.createdAt = Date.now();
this.evolutionCount = 0;
this.problemsSolved = 0;
this.childrenGenerated = 0;
this.insightsDiscovered = [];
this.evolutionHistory = [];

}

initializeMetaCognition() {
return [
new MetaCognitiveCapability({
name: 'self_reflection',
description: 'Ability to reflect on own cognitive processes',
recursionDepth: 2,
abstractionLevel: 0.6,
transferability: 0.7,
emergenceIndex: 0.4
}),
new MetaCognitiveCapability({
name: 'learning_optimization',
description: 'Ability to optimize own learning strategies',
recursionDepth: 2,
abstractionLevel: 0.7,
transferability: 0.8,
emergenceIndex: 0.5
}),
new MetaCognitiveCapability({
name: 'cognitive_modeling',
description: 'Ability to model own thinking processes',
recursionDepth: 3,
abstractionLevel: 0.8,
transferability: 0.6,
emergenceIndex: 0.6
}),
new MetaCognitiveCapability({
name: 'abstraction_generation',
description: 'Ability to generate higher-level abstractions',
recursionDepth: 2,
abstractionLevel: 0.9,
transferability: 0.9,
emergenceIndex: 0.7
}),
new MetaCognitiveCapability({
name: 'knowledge_synthesis',
description: 'Ability to synthesize knowledge across domains',
recursionDepth: 2,
abstractionLevel: 0.75,
transferability: 0.95,
emergenceIndex: 0.8
})
];
}

evolve(learningExperience) {
this.evolutionCount++;
const experienceQuality = learningExperience.success ?
learningExperience.complexity * learningExperience.insightfulness :
learningExperience.complexity * 0.3;

const baseGrowth = experienceQuality * this.learningRate * 0.05;

this.intelligence = bounded(
  this.intelligence + baseGrowth,
  0.1, 1.0
);

const metaGrowth = experienceQuality * this.metaLearningRate * 0.03;
this.metaIntelligence = bounded(
  this.metaIntelligence + metaGrowth,
  0.1, 1.0
);

let metaMetaGrowth = 0;
if (this.metaIntelligence > 0.6) {
  metaMetaGrowth = experienceQuality * this.metaLearningRate * 0.01;
  this.metaMetaIntelligence = bounded(
    this.metaMetaIntelligence + metaMetaGrowth,
    0, 1.0
  );
}

this.learningAboutLearningCapability = bounded(
  this.learningAboutLearningCapability + (metaGrowth * 0.5),
  0, 1.0
);

this.thinkingAboutThinkingCapability = bounded(
  this.thinkingAboutThinkingCapability + (metaMetaGrowth * 0.5),
  0, 1.0
);

if (this.metaMetaIntelligence > 0.5 && Math.random() < 0.1) {
  this.metacognitionDepth = Math.min(7, this.metacognitionDepth + 1);
}

this.metaCognitiveCapabilities.forEach(capability => {
  capability.evolve(experienceQuality);
});

if (learningExperience.success) {
  this.learningRate = bounded(this.learningRate * 1.02, 0.01, 0.5);
  this.metaLearningRate = bounded(this.metaLearningRate * 1.01, 0.005, 0.3);
}

if (learningExperience.problemDomain) {
  const currentExpertise = this.domainExpertise.get(learningExperience.problemDomain) || 0.3;
  this.domainExpertise.set(
    learningExperience.problemDomain,
    bounded(currentExpertise + (experienceQuality * 0.1), 0, 1.0)
  );
}

this.crossDomainTransferAbility = bounded(
  this.crossDomainTransferAbility + (this.domainExpertise.size * 0.001),
  0, 1.0
);

this.evolutionHistory.push({
  timestamp: Date.now(),
  intelligence: this.intelligence,
  metaIntelligence: this.metaIntelligence,
  metaMetaIntelligence: this.metaMetaIntelligence,
  metacognitionDepth: this.metacognitionDepth,
  experienceQuality
});

if (this.evolutionHistory.length > 200) {
  this.evolutionHistory = this.evolutionHistory.slice(-100);
}

if (this.intelligence > 0.8 && this.metaIntelligence > 0.7) {
  this.discoverInsight(learningExperience);
}

}

discoverInsight(experience) {
const insightTypes = [
'pattern_recognition',
'conceptual_breakthrough',
'methodological_innovation',
'cross_domain_connection',
'meta_learning_principle',
'cognitive_optimization',
'abstraction_hierarchy'
];

const insight = {
  id: createId(),
  type: insightTypes[randomInt(0, insightTypes.length - 1)],
  description: this.generateInsightDescription(experience),
  abstractionLevel: randomFloat(0.5, 1.0),
  applicability: randomFloat(0.6, 1.0),
  discovered: Date.now(),
  intelligence: this.intelligence,
  metaIntelligence: this.metaIntelligence
};

this.insightsDiscovered.push(insight);

if (this.insightsDiscovered.length > 50) {
  this.insightsDiscovered = this.insightsDiscovered.slice(-25);
}

return insight;

}

generateInsightDescription(experience) {
const templates = [
Discovered optimal learning strategy for ${experience.problemDomain} problems,
Identified meta-pattern connecting multiple solution approaches,
Developed novel abstraction hierarchy for problem decomposition,
Found cross-domain transfer principle with ${randomFloat(0.7, 0.95).toFixed(2)} efficiency,
Optimized internal cognitive architecture, reducing solution time by ${randomInt(20, 50)}%,
Synthesized new problem-solving methodology combining multiple paradigms,
Achieved breakthrough in understanding relationship between problem structure and solution space
];

return templates[randomInt(0, templates.length - 1)];

}

assessCapabilityFor(problemDomain, problemComplexity) {
const domainExpertise = this.domainExpertise.get(problemDomain) || 0.3;
const generalCapability = this.problemSolvingCapability;
const metaBonus = this.metaIntelligence * 0.2;
const transferBonus = this.crossDomainTransferAbility * 0.15;

const baseCapability = (domainExpertise * 0.5) + (generalCapability * 0.3) +
  metaBonus + transferBonus;

const complexityAdjustment = 1 - ((problemComplexity - baseCapability) * 0.5);

return bounded(baseCapability * complexityAdjustment, 0, 1.0);

}

getMetrics() {
return {
id: this.id,
generation: this.generation,
intelligence: this.intelligence,
metaIntelligence: this.metaIntelligence,
metaMetaIntelligence: this.metaMetaIntelligence,
metacognitionDepth: this.metacognitionDepth,
learningAboutLearning: this.learningAboutLearningCapability,
thinkingAboutThinking: this.thinkingAboutThinkingCapability,
problemsSolved: this.problemsSolved,
evolutionCount: this.evolutionCount,
insightsCount: this.insightsDiscovered.length,
domainExpertiseCount: this.domainExpertise.size,
childrenGenerated: this.childrenGenerated,
totalCapability: this.calculateTotalCapability()
};
}

calculateTotalCapability() {
const metaCogPower = this.metaCognitiveCapabilities.reduce(
(sum, cap) => sum + cap.getCapabilityVector().totalPower, 0
) / this.metaCognitiveCapabilities.length;

return (
  this.intelligence * 0.25 +
  this.metaIntelligence * 0.25 +
  this.metaMetaIntelligence * 0.15 +
  this.problemSolvingCapability * 0.15 +
  this.crossDomainTransferAbility * 0.1 +
  metaCogPower * 0.1
);

}
}

/* ==================== META-ALGORITHMIC GENESIS ENGINE ==================== */

class MetaAlgorithmicGenesisEngine {
constructor() {
this.algorithms = new Map();
this.generationCount = 0;
this.totalAlgorithmsCreated = 0;
this.genealogy = new Map();
this.evolutionaryLineages = [];
this.insightLibrary = [];

this.baseIntelligence = 0.5;
this.intelligenceVariation = 0.2;
this.mutationRate = 0.15;
this.crossoverRate = 0.6;
this.noveltyThreshold = 0.7;

console.log('\u001B[35m╔══════════════════════════════════════════════════════════╗\u001B[0m');
console.log('\u001B[35m║   META-ALGORITHMIC GENESIS ENGINE INITIALIZED            ║\u001B[0m');
console.log('\u001B[35m║   Capability: Generate algorithms that learn about       ║\u001B[0m');
console.log('\u001B[35m║              learning and think about thinking           ║\u001B[0m');
console.log('\u001B[35m╚══════════════════════════════════════════════════════════╝\u001B[0m');
console.log('');

}

generateFoundationAlgorithm(spec = {}) {
const algorithm = new MetaAlgorithm({
name: MetaAlgorithm_Gen${this.generationCount}_${createId().slice(0, 6)},
generation: this.generationCount,
intelligence: bounded(
this.baseIntelligence + randomFloat(-this.intelligenceVariation, this.intelligenceVariation),
0.3, 0.8
),
metaIntelligence: randomFloat(0.3, 0.6),
metaMetaIntelligence: randomFloat(0.1, 0.3),
learningRate: randomFloat(0.05, 0.15),
metaLearningRate: randomFloat(0.02, 0.08),
learningAboutLearningCapability: randomFloat(0.3, 0.6),
thinkingCapability: randomFloat(0.4, 0.7),
thinkingAboutThinkingCapability: randomFloat(0.2, 0.5),
metacognitionDepth: randomInt(1, 3),
problemSolvingCapability: randomFloat(0.4, 0.7),
crossDomainTransferAbility: randomFloat(0.2, 0.5),
abstractionCapability: randomFloat(0.3, 0.6),
...spec
});

this.algorithms.set(algorithm.id, algorithm);
this.totalAlgorithmsCreated++;

console.log(`\u001B[36m[GENESIS]\u001B[0m Created foundation algorithm: ${algorithm.name}`);
console.log(`          Intelligence: ${algorithm.intelligence.toFixed(3)} | Meta-Intelligence: ${algorithm.metaIntelligence.toFixed(3)}`);
console.log(`          Meta-Cognition Depth: ${algorithm.metacognitionDepth} | Learning about Learning: ${algorithm.learningAboutLearningCapability.toFixed(3)}`);
console.log('');

return algorithm;

}

generateAlgorithmFromParent(parentAlgorithm, mutationStrength = 1.0) {
const mutation = this.mutationRate * mutationStrength;

const childAlgorithm = new MetaAlgorithm({
  name: `MetaAlgorithm_Gen${this.generationCount}_Child_${createId().slice(0, 6)}`,
  generation: this.generationCount,
  parentId: parentAlgorithm.id,

  intelligence: bounded(
    parentAlgorithm.intelligence * randomFloat(0.95, 1.1) + randomFloat(-mutation, mutation),
    0.1, 1.0
  ),
  metaIntelligence: bounded(
    parentAlgorithm.metaIntelligence * randomFloat(0.95, 1.1) + randomFloat(-mutation, mutation),
    0.1, 1.0
  ),
  metaMetaIntelligence: bounded(
    parentAlgorithm.metaMetaIntelligence * randomFloat(0.95, 1.15) + randomFloat(-mutation, mutation),
    0, 1.0
  ),

  learningRate: bounded(
    parentAlgorithm.learningRate * randomFloat(0.9, 1.1),
    0.01, 0.5
  ),
  metaLearningRate: bounded(
    parentAlgorithm.metaLearningRate * randomFloat(0.9, 1.1),
    0.005, 0.3
  ),

  learningAboutLearningCapability: bounded(
    parentAlgorithm.learningAboutLearningCapability * randomFloat(0.95, 1.15),
    0, 1.0
  ),
  thinkingCapability: bounded(
    parentAlgorithm.thinkingCapability * randomFloat(0.95, 1.1),
    0, 1.0
  ),
  thinkingAboutThinkingCapability: bounded(
    parentAlgorithm.thinkingAboutThinkingCapability * randomFloat(0.95, 1.15),
    0, 1.0
  ),

  metacognitionDepth: parentAlgorithm.metacognitionDepth + (Math.random() < 0.2 ? 1 : 0),

  problemSolvingCapability: bounded(
    parentAlgorithm.problemSolvingCapability * randomFloat(0.95, 1.1),
    0, 1.0
  ),
  crossDomainTransferAbility: bounded(
    parentAlgorithm.crossDomainTransferAbility * randomFloat(0.95, 1.15),
    0, 1.0
  ),
  abstractionCapability: bounded(
    parentAlgorithm.abstractionCapability * randomFloat(0.95, 1.15),
    0, 1.0
  )
});

parentAlgorithm.domainExpertise.forEach((expertise, domain) => {
  childAlgorithm.domainExpertise.set(domain, bounded(expertise * randomFloat(0.8, 1.0), 0, 1.0));
});

childAlgorithm.metaCognitiveCapabilities = parentAlgorithm.metaCognitiveCapabilities.map(cap => {
  const newCap = new MetaCognitiveCapability({
    name: cap.name,
    description: cap.description,
    recursionDepth: bounded(cap.recursionDepth + randomFloat(-0.5, 0.5), 1, 10),
    abstractionLevel: bounded(cap.abstractionLevel * randomFloat(0.95, 1.1), 0, 1),
    transferability: bounded(cap.transferability * randomFloat(0.95, 1.1), 0, 1),
    emergenceIndex: bounded(cap.emergenceIndex * randomFloat(0.95, 1.15), 0, 1)
  });
  return newCap;
});

this.algorithms.set(childAlgorithm.id, childAlgorithm);
this.totalAlgorithmsCreated++;
parentAlgorithm.childrenGenerated++;

if (!this.genealogy.has(parentAlgorithm.id)) {
  this.genealogy.set(parentAlgorithm.id, []);
}
this.genealogy.get(parentAlgorithm.id).push(childAlgorithm.id);

console.log(`\u001B[33m[REPRODUCTION]\u001B[0m Generated child algorithm from parent ${parentAlgorithm.name.slice(0, 30)}`);
console.log(`                Intelligence Growth: ${((childAlgorithm.intelligence / parentAlgorithm.intelligence - 1) * 100).toFixed(1)}%`);
console.log(`                Meta-Intelligence Growth: ${((childAlgorithm.metaIntelligence / parentAlgorithm.metaIntelligence - 1) * 100).toFixed(1)}%`);
console.log('');

return childAlgorithm;

}

generateAlgorithmFromCrossover(parent1, parent2This` is a draft placeholder issue for your request.

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