MMicro Mind

Architecture Document

Fitness Model V2

Multi-dimensional selection for nodes, recipes, and workflows

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FITNESS MODEL V2

Vision

The Fitness Model is the decision-making system that determines which nodes survive, which recipes evolve, which workflows spread, and which procedural knowledge becomes part of the Micro Mind organism.

Without fitness there is no selection.

Without selection there is no evolution.

The Fitness Model is therefore one of the three core pillars of Micro Mind:

Memory
+
Fitness
+
Adaptive Node Evolution Loop
=
Evolutionary Procedural Intelligence

Purpose

Most automation systems evaluate success using a binary result:

Success
or
Failure

Micro Mind uses a multi-dimensional fitness model.

A workflow that succeeds may still be inefficient.

A workflow that is fast may still be dangerous.

A workflow that is reliable may still waste resources.

Fitness exists to measure overall value rather than simple completion.

Core Principle

The objective is not:

Maximum Success

The objective is:

Maximum Success
Minimum Time
Minimum Cost
Minimum Energy
Minimum Resource Waste
Minimum Human Effort
Minimum Environmental Impact
Maximum Reliability
Maximum Reusability

The best workflow is not the workflow that merely works.

The best workflow is the workflow that produces the highest value with the lowest waste.

Fitness Dimensions

Success Fitness

Measures:

  • Task completion rate
  • Verification success rate
  • Validation success rate
  • Long-term stability

Questions:

  • Did the task succeed?
  • Was the result correct?
  • Was the result repeatable?

Time Fitness

Measures:

  • Execution duration
  • Time to completion
  • Human waiting time

Questions:

  • How quickly was the result achieved?
  • Could it have been completed faster?

Cost Fitness

Measures:

  • API costs
  • Cloud costs
  • Compute costs
  • Infrastructure costs

Questions:

  • How expensive was execution?
  • Can the same result be achieved more cheaply?

Energy Fitness

Measures:

  • CPU consumption
  • GPU consumption
  • Estimated power usage
  • Resource intensity

Questions:

  • How much energy was required?
  • Could the same result be achieved more efficiently?

Resource Fitness

Measures:

  • Memory usage
  • Storage usage
  • Network usage
  • External dependency count

Questions:

  • How many resources were consumed?
  • Were resources wasted?

Human Dependency Fitness

Measures:

  • Human approvals required
  • Human corrections required
  • Manual decisions required

Questions:

  • How autonomous is the workflow?
  • How often does it require intervention?

Reliability Fitness

Measures:

  • Repeatability
  • Error rate
  • Failure recovery
  • Stability over time

Questions:

  • Does it consistently work?
  • Does it degrade under stress?

Reusability Fitness

Measures:

  • Cross-project applicability
  • Cross-domain applicability
  • Recipe portability

Questions:

  • Can this solution be reused elsewhere?
  • Does it create general procedural knowledge?

Environmental Fitness

Measures:

  • Energy waste
  • Resource waste
  • Infrastructure impact

Questions:

  • Is the workflow unnecessarily expensive to operate?
  • Is there a lower-impact alternative?

Composite Fitness Score

No single metric should dominate the system.

Fitness should be calculated from multiple dimensions.

Conceptually:

Fitness
=
Success
+ Time
+ Cost
+ Energy
+ Resources
+ Human Dependency
+ Reliability
+ Reusability
+ Environmental Impact

Weights may evolve over time.

Different recipe species may use different weighting models.

Fitness and Nodes

Every node maintains fitness statistics.

Example:

{
  "node": "FirebaseSha1VerifierNode",
  "activation_count": 312,
  "success_rate": 0.98,
  "avg_duration": 2.1,
  "fitness_score": 0.91,
  "state": "promoted"
}

Possible states:

  • Candidate
  • Active
  • Promoted
  • Sleeping
  • Archived

Nodes survive through fitness.

Fitness and Recipes

Recipes compete against other recipes inside the same species.

Example:

FirebaseSetupSpecies
├── Recipe V1
├── Recipe V2
├── Recipe V3

Each recipe accumulates fitness over time.

Higher-fitness recipes receive more opportunities.

Lower-fitness recipes gradually enter sleep or archive states.

Fitness and ANEL

The Adaptive Node Evolution Loop depends on fitness.

Observation
↓
Candidate Node Birth
↓
Simulation
↓
Verification
↓
Fitness Evaluation
↓
Selection
↓
Mutation
↓
Evolution

Fitness is the mechanism that decides:

  • What survives
  • What spreads
  • What mutates
  • What disappears

Evolutionary Selection

Selection outcomes:

Promote

Fitness exceeds threshold.

Node or recipe becomes active.

Sleep

Potential exists but evidence is insufficient.

Stored for future reactivation.

Archive

Fitness consistently underperforms.

Knowledge is preserved but removed from active evolution.

Human Feedback as Fitness Signal

Human behavior is a valuable fitness source.

Examples:

  • Manual corrections
  • Repeated overrides
  • Explicit approvals
  • Explicit rejections

Human actions provide evolutionary pressure that guides procedural adaptation.

Long-Term Objective

The Fitness Model exists to drive continuous procedural improvement.

Its purpose is to help Micro Mind discover increasingly better ways of performing tasks while balancing:

  • Success
  • Speed
  • Cost
  • Energy
  • Resource efficiency
  • Human effort
  • Reliability
  • Environmental responsibility

The ultimate goal is not automation.

The ultimate goal is adaptive optimization through evolution.

Summary

The Fitness Model is the selection engine of Micro Mind.

Memory remembers.

ANEL creates and evolves.

Fitness decides what survives.

Together they form the foundation of an evolutionary procedural intelligence system.