01-09-2026, 03:30 PM
## Nature’s Algorithms: Evolution as Computation
For centuries, nature was viewed as chaotic — a messy tangle of organisms, environments, and chance events. Computation, by contrast, was seen as clean and artificial: symbols, rules, and machines created by human minds.
Modern science has quietly inverted this view.
Nature does not merely *contain* computation.
Nature **performs** computation — constantly, relentlessly, and at planetary scale.
Nowhere is this more striking than in evolution.
---
### 1. What Is an Algorithm?
At its core, an algorithm is not a computer program.
An algorithm is simply:
- a **set of rules**
- that **transform inputs into outputs**
- through **iteration**
- guided by **selection or evaluation**
Sorting numbers is an algorithm.
Training a neural network is an algorithm.
Searching for the shortest path is an algorithm.
So is evolution.
---
### 2. Evolution Has All the Parts of an Algorithm
Evolution operates through four fundamental steps:
1. **Variation**
Genetic mutations introduce differences between individuals.
2. **Replication**
Organisms reproduce, copying genetic information with noise.
3. **Evaluation**
The environment “tests” which variants survive and reproduce.
4. **Selection**
Successful variants propagate; unsuccessful ones vanish.
This loop repeats over generations.
That is not metaphorical.
That is an algorithm.
---
### 3. Evolution as a Search Process
In computer science, many problems are too complex to solve exactly. Instead, algorithms **search** a vast space of possibilities.
Evolution does the same.
- The space: all possible genetic configurations
- The objective function: reproductive success (fitness)
- The constraints: physics, chemistry, environment, competition
Evolution does not know the goal in advance.
It **discovers** solutions by exploring what works.
This is identical in structure to:
- genetic algorithms
- evolutionary optimisation
- reinforcement learning
Humans did not invent these ideas.
We copied them from life.
---
### 4. Fitness Landscapes: Nature’s Cost Function
Imagine a landscape where:
- position = genetic configuration
- height = reproductive success
This is called a **fitness landscape**.
Evolution behaves like an algorithm that:
- climbs uphill when selection is strong
- wanders randomly when selection is weak
- can get trapped on local peaks
- sometimes crosses valleys via chance
This explains:
- why evolution is not optimal
- why history matters
- why “good enough” solutions dominate biology
Nature does not compute perfection.
It computes **viability**.
---
### 5. Mutation as Randomised Exploration
In algorithms, randomness is not a bug — it is a feature.
Randomised algorithms:
- escape local optima
- explore unknown regions
- avoid brittle solutions
Mutation plays exactly this role.
Most mutations are neutral or harmful.
A tiny fraction are beneficial.
Without mutation, evolution would freeze.
Mathematically, mutation acts like:
- noise injected into the search process
- a stochastic perturbation term
This makes evolution a **probabilistic algorithm**, not a deterministic one.
---
### 6. Selection as an Update Rule
In computation, an update rule adjusts future behaviour based on performance.
Evolution’s update rule is brutal but simple:
- variants that reproduce more → increase in frequency
- variants that reproduce less → decrease in frequency
If p is the frequency of a genetic variant, then its change over time follows:
Δp ≈ p(1 − p) × selection pressure
This is not philosophy.
It is a quantitative rule.
Evolution updates population states the way optimisation algorithms update parameters.
---
### 7. Drift: When the Algorithm Becomes Noisy
Not all evolution is driven by selection.
In small populations, **genetic drift** dominates:
- random sampling effects
- chance survival
- founder effects
This is equivalent to:
- stochastic error in computation
- finite sample noise
- Monte Carlo variability
Drift explains why:
- traits can spread without advantage
- populations diverge unpredictably
- history leaves permanent fingerprints
Nature’s algorithm is noisy — and that noise matters.
---
### 8. Parallelism at Planetary Scale
Computers struggle with massive parallelism.
Evolution does not.
Every organism is:
- a separate “processor”
- running in parallel
- evaluating a different solution
- under real-world constraints
At any moment:
- billions of experiments are running
- trillions of genetic variants are tested
- failures are discarded instantly
Evolution is the most powerful parallel computation known.
And it runs on sunlight.
---
### 9. Information Storage Without a Central Processor
There is no central controller in evolution.
No master program.
No blueprint.
Information is:
- distributed across genomes
- encoded chemically
- updated locally
- preserved through reproduction
This is **distributed computation**, long before humans named it.
Life stores knowledge about the environment:
- in proteins
- in regulatory networks
- in developmental pathways
A genome is not a plan.
It is a **compressed record of past success**.
---
### 10. Why Evolution Looks Intelligent (But Isn’t)
Evolution produces:
- eyes
- brains
- immune systems
- ecosystems
These systems appear designed.
But algorithmic processes can generate structure without foresight.
Just as:
- neural networks learn without understanding
- optimisation algorithms find solutions blindly
Evolution creates complexity without intention.
This is why:
- intelligence can emerge from mindless rules
- order can arise from randomness
- purpose is an illusion layered on process
---
### 11. Limits of Nature’s Algorithm
Evolution is powerful — but constrained.
It cannot:
- plan ahead
- reset from scratch
- undo history
- escape physical laws
This explains:
- inefficient designs (like the human spine)
- fragile systems
- evolutionary “kludges”
Nature optimises locally, not globally.
That is exactly how heuristic algorithms behave.
---
### 12. Humans Are Part of the Algorithm
Our brains evolved by the same process.
Our intelligence is a product of nature’s computation.
When we build:
- AI
- optimisation algorithms
- learning systems
We are not opposing nature.
We are **extending it**.
Evolution wrote the first algorithms.
We are now running variations of them — consciously.
---
### 13. A Profound Inversion
We often say:
“Evolution is like computation.”
The deeper truth is the reverse:
**Computation is a special case of evolution.**
Algorithms, learning, optimisation, intelligence — all are shadows of a deeper process that life discovered billions of years ago.
---
### 14. The Terrifying Implication
If evolution is computation, then:
- intelligence does not require intention
- complexity does not require design
- meaning does not require foresight
And nothing guarantees the algorithm favours us.
Nature computes outcomes, not values.
---
### 15. Final Thought
Evolution is not a story about survival.
It is a story about **information processing under constraint**.
Life is not just chemistry.
It is not just biology.
Life is computation, running on matter, shaped by chance, filtered by reality.
And we are both its product — and its next experiment.
For centuries, nature was viewed as chaotic — a messy tangle of organisms, environments, and chance events. Computation, by contrast, was seen as clean and artificial: symbols, rules, and machines created by human minds.
Modern science has quietly inverted this view.
Nature does not merely *contain* computation.
Nature **performs** computation — constantly, relentlessly, and at planetary scale.
Nowhere is this more striking than in evolution.
---
### 1. What Is an Algorithm?
At its core, an algorithm is not a computer program.
An algorithm is simply:
- a **set of rules**
- that **transform inputs into outputs**
- through **iteration**
- guided by **selection or evaluation**
Sorting numbers is an algorithm.
Training a neural network is an algorithm.
Searching for the shortest path is an algorithm.
So is evolution.
---
### 2. Evolution Has All the Parts of an Algorithm
Evolution operates through four fundamental steps:
1. **Variation**
Genetic mutations introduce differences between individuals.
2. **Replication**
Organisms reproduce, copying genetic information with noise.
3. **Evaluation**
The environment “tests” which variants survive and reproduce.
4. **Selection**
Successful variants propagate; unsuccessful ones vanish.
This loop repeats over generations.
That is not metaphorical.
That is an algorithm.
---
### 3. Evolution as a Search Process
In computer science, many problems are too complex to solve exactly. Instead, algorithms **search** a vast space of possibilities.
Evolution does the same.
- The space: all possible genetic configurations
- The objective function: reproductive success (fitness)
- The constraints: physics, chemistry, environment, competition
Evolution does not know the goal in advance.
It **discovers** solutions by exploring what works.
This is identical in structure to:
- genetic algorithms
- evolutionary optimisation
- reinforcement learning
Humans did not invent these ideas.
We copied them from life.
---
### 4. Fitness Landscapes: Nature’s Cost Function
Imagine a landscape where:
- position = genetic configuration
- height = reproductive success
This is called a **fitness landscape**.
Evolution behaves like an algorithm that:
- climbs uphill when selection is strong
- wanders randomly when selection is weak
- can get trapped on local peaks
- sometimes crosses valleys via chance
This explains:
- why evolution is not optimal
- why history matters
- why “good enough” solutions dominate biology
Nature does not compute perfection.
It computes **viability**.
---
### 5. Mutation as Randomised Exploration
In algorithms, randomness is not a bug — it is a feature.
Randomised algorithms:
- escape local optima
- explore unknown regions
- avoid brittle solutions
Mutation plays exactly this role.
Most mutations are neutral or harmful.
A tiny fraction are beneficial.
Without mutation, evolution would freeze.
Mathematically, mutation acts like:
- noise injected into the search process
- a stochastic perturbation term
This makes evolution a **probabilistic algorithm**, not a deterministic one.
---
### 6. Selection as an Update Rule
In computation, an update rule adjusts future behaviour based on performance.
Evolution’s update rule is brutal but simple:
- variants that reproduce more → increase in frequency
- variants that reproduce less → decrease in frequency
If p is the frequency of a genetic variant, then its change over time follows:
Δp ≈ p(1 − p) × selection pressure
This is not philosophy.
It is a quantitative rule.
Evolution updates population states the way optimisation algorithms update parameters.
---
### 7. Drift: When the Algorithm Becomes Noisy
Not all evolution is driven by selection.
In small populations, **genetic drift** dominates:
- random sampling effects
- chance survival
- founder effects
This is equivalent to:
- stochastic error in computation
- finite sample noise
- Monte Carlo variability
Drift explains why:
- traits can spread without advantage
- populations diverge unpredictably
- history leaves permanent fingerprints
Nature’s algorithm is noisy — and that noise matters.
---
### 8. Parallelism at Planetary Scale
Computers struggle with massive parallelism.
Evolution does not.
Every organism is:
- a separate “processor”
- running in parallel
- evaluating a different solution
- under real-world constraints
At any moment:
- billions of experiments are running
- trillions of genetic variants are tested
- failures are discarded instantly
Evolution is the most powerful parallel computation known.
And it runs on sunlight.
---
### 9. Information Storage Without a Central Processor
There is no central controller in evolution.
No master program.
No blueprint.
Information is:
- distributed across genomes
- encoded chemically
- updated locally
- preserved through reproduction
This is **distributed computation**, long before humans named it.
Life stores knowledge about the environment:
- in proteins
- in regulatory networks
- in developmental pathways
A genome is not a plan.
It is a **compressed record of past success**.
---
### 10. Why Evolution Looks Intelligent (But Isn’t)
Evolution produces:
- eyes
- brains
- immune systems
- ecosystems
These systems appear designed.
But algorithmic processes can generate structure without foresight.
Just as:
- neural networks learn without understanding
- optimisation algorithms find solutions blindly
Evolution creates complexity without intention.
This is why:
- intelligence can emerge from mindless rules
- order can arise from randomness
- purpose is an illusion layered on process
---
### 11. Limits of Nature’s Algorithm
Evolution is powerful — but constrained.
It cannot:
- plan ahead
- reset from scratch
- undo history
- escape physical laws
This explains:
- inefficient designs (like the human spine)
- fragile systems
- evolutionary “kludges”
Nature optimises locally, not globally.
That is exactly how heuristic algorithms behave.
---
### 12. Humans Are Part of the Algorithm
Our brains evolved by the same process.
Our intelligence is a product of nature’s computation.
When we build:
- AI
- optimisation algorithms
- learning systems
We are not opposing nature.
We are **extending it**.
Evolution wrote the first algorithms.
We are now running variations of them — consciously.
---
### 13. A Profound Inversion
We often say:
“Evolution is like computation.”
The deeper truth is the reverse:
**Computation is a special case of evolution.**
Algorithms, learning, optimisation, intelligence — all are shadows of a deeper process that life discovered billions of years ago.
---
### 14. The Terrifying Implication
If evolution is computation, then:
- intelligence does not require intention
- complexity does not require design
- meaning does not require foresight
And nothing guarantees the algorithm favours us.
Nature computes outcomes, not values.
---
### 15. Final Thought
Evolution is not a story about survival.
It is a story about **information processing under constraint**.
Life is not just chemistry.
It is not just biology.
Life is computation, running on matter, shaped by chance, filtered by reality.
And we are both its product — and its next experiment.