Welcome to the deep dive. Today, we are looking
at this stack of research that basically proves
your computer's most powerful tool isn't its
flawless logic. It's actually, well, its ability
to make random terrible mistakes. Right, which
completely flips the script on how we usually
think about technology. I mean, we expect computers
to be these rigid, you know, highly engineered
machines that just follow explicit instructions
to spit out one perfect answer. Yeah, exactly.
But the incredibly detailed Wikipedia deep dive
we were exploring today shows us a totally different
paradigm. We are diving into evolutionary computation.
This is a field where computer scientists literally
stole Mother Nature's playbook. They really did.
They're using Darwinian evolution. you know,
survival of the fittest to solve the world's
most complex computational problems. So our mission
today is to figure out how they're actually doing
this, why making random mistakes is somehow the
secret to hyperintelligence, and ultimately what
this means for you and the technology you use
every single day. Because the underlying philosophy
here, it turns a rigid calculator into a trial
and error problem solver. It's literally the
process of artificial natural selection. The
machine isn't calculating a single answer through
a fixed formula. It's growing an answer organically
over time. OK, let's unpack this. Because the
mechanics of how this actually works in silicon
are just wild to me. If you look at how this
is modeled, there are three foundational forces
at play. You've got recombination, mutation,
and selection. Yeah, think of those three forces
as the engine of evolution. So recombination
is taking traits from two successful parents
and just mixing them together. Then mutation
introduces a totally random, unpredictable change
to the data. Just a mistake, basically. And selection
is the environment ruthlessly killing off the
weak cord, allowing only the highest quality
solutions to survive to the next round. To visualize
this, there is this classic animation used to
demonstrate the concept. The computer is asked
to evolve an image of Charles Darwin's face.
And the fascinating part is that it doesn't start
with a sketch. No, not at all. It starts with
a massive population of completely random grayscale
patches. Like just pure visual static. Great,
because the programmer never tells the machine
how to draw a face. They only define a goal,
which is resembling Darwin, and a mechanism for
survival. So the computer ranks that static based
on how closely it accidentally resembles the
target. It trashes the worst static, takes the
best performing patches, and breeds them together.
It mixes their visual data, so that's the recombination.
Exactly. And then it throws in a few random pixel
changes, the mutation. Generation by generation,
the noise evolves. Eyes emerge. A beard forms.
And eventually, you are staring at a perfect
portrait of Charles Darwin. Honestly, it reminds
me of breeding racehorses. That is actually a
very accurate way to frame it. Right, because
if you want a fast horse with incredible stamina,
you don't build one from scratch in a lab. You
take a ridiculously fast horse and breed it with
a horse that can run all day. And you just hope
the offspring inherits both traits through that
recombination. Yeah, and maybe a random genetic
mutation gives the new full slightly larger lungs
or something. You waste them, you keep the winners,
and you put the losers out to pasture. You are
mathematically speaking isolating the winning
traits and combining them. Wait, I have to ask
about the efficiency here. Yeah. I mean, calculating
millions of artificial horse races in a computer
seems incredibly slow and honestly chaotic. It
can seem that way, yeah. Why go through all this
trial and error? Why not just write an algebraic
formula to find the mathematically perfect horse
or that perfect visual pixel arrangement directly?
Well, what's fascinating here is that traditional
math completely breaks down when problems become
multi -dimensional. Think about standard optimization
algorithms like gradient descent, for example.
OK. They are fantastic at finding an answer,
but they frequently get trapped in what computer
scientists call local minima. Local minima. Give
me a visual for that. How does a computer get
trapped? Okay, imagine you are blindfolded, and
your only goal is to find the absolute lowest
point in this massive, rugged mountain range.
The traditional computer logic is simple. You
feel the ground with your foot, and you always
take a step downhill. I mean, that makes intuitive
sense. If you always go down, you— eventually
hit the bottom of the valley. Usually, yes. But
what if you walk downhill and hit the bottom
of a small crater halfway up the mountain? Ooh!
Right. Every direction you step from the bottom
of that crater is uphill. So the traditional
algorithm feels the ground, realizes it can't
go downhill anymore, and declares, I must be
at the absolute lowest point on earth, problem
solved. So it settles for a good but not best
solution. It's physically trapped by its own
rigid logic. Precisely. It stops searching entirely
because the formula tells it the job is done.
Wow. And this is where evolutionary computation
comes in. It avoids this trap by using mutation.
Mutation is a random, unpredictable jolt. Like
an earthquake. Exactly. It's a sudden earthquake
that bounces you entirely out of that crater,
throwing you to a completely different part of
the mountain range so you can keep exploring
and find the true global valley floor. Without
that random mistake, the system literally cannot
find the best answer. To realize that a computer
needed to make random mistakes to avoid those
craters, that is a massive leap in logic. Especially
when you consider the history. I mean, the computers
of the 1950s and 60s were these massive, room
-sized, incredibly expensive calculators. Yeah,
they were precious resources. You did not just
tell a mainframe to act randomly. So who had
the guts to actually try this? Well, the concept
of mimicking evolution in a machine goes all
the way back to Alan Turing in 1948. Turing.
Like the code breaker, the father of modern computing.
The very same. He was decades ahead of his time.
Turing proposed what he called B -type and P
-type unorganized machines. Unorganized machines?
Yeah, he was essentially mapping out primitive
neural networks where the connections between
nodes were learned via a genetic search. He designed
them to use pleasure and pain signals to learn
behaviors. Wait, pleasure and pain signals in
1948? Why isn't Turing universally recognized
as the founder of evolutionary computation? Oh,
it is a matter of tragic timing. Turing passed
away in 1954, and the brilliant 1948 paper of
his went entirely unpublished until 1968. Oh,
wow. It just sat in a drawer while the rest of
the world caught it. Exactly. It had essentially
zero impact on the early development of the field.
Evolutionary computation had to be completely
reinvented by independent researchers who had
absolutely no idea Turing had already mapped
the Turin. Which brings us to 1964, Germany.
Two researchers, named Ingo Reckenberg and Hans
-Paul Schweiffel, are trying to solve incredibly
complex optimization problems in fluid dynamics.
And they are the ones who really kick off a branch
called evolution strategies. Right, because fluid
dynamics is notoriously difficult to calculate.
The math is highly non -linear. If you are trying
to design the perfect shape for a pipe or, say,
an airplane wing, a tiny change in the curve
creates massive unpredictable changes in how
the air or water flows. You cannot easily use
a rigid formula to find the perfect shape. So
they decided to use evolution. But the craziest
part is, they didn't even have a computer to
run the simulations at first. Oh, they didn't.
They were sitting in a lab, literally rolling
physical dice to determine the random mutations
for their pipe shapes. Rolling dice to introduce
that earthquake, that random jolt out of the
crater, they would change a parameter based on
the dice roll, manually test the physical result,
and then just keep the shape that performed best.
It sounds incredibly tedious. It does. But it
proved that the fundamental math of evolution
works regardless of the medium. You do not need
silicon to run an evolutionary algorithm. Wow.
And as computers did become more powerful through
the 1960s and 70s, it seems like different scientists
across the globe started building their own distinct
evolutionary religions, so to speak. They all
realized Darwinian math worked, but they had
wildly different grades of coding it. Yeah, the
global isolation during that era led to three
distinct historic branches developing simultaneously.
We have Reckenberg and Schreiffel in Germany,
rolling dice for engineering parameters. But
over in the United States around 1962, you have
Lawrence J. Fogel. Developing evolutionary programming.
Yes. Fogel was focused on prediction problems.
Yeah. And the documentation notes he wasn't mutating
physical parameters. He was mutating finite state
machines. Can you break that down for us? Sure.
A finite state machine is a mathematical model
of computation. Think of it like a subway turnstile.
Okay. It has a finite number of states. It is
either locked or unlocked. If you insert a coin,
the state transitions from locked to unlocked.
If you push the arm, it transitions back to locked.
It is a system of rigid rules and triggers. So
Fogle would take an artificial intelligence running
on a complex version of those rules. Instead
of changing the data, he would randomly mutate
the rules themselves. Exactly. Like spontaneously
adding a new state. or deleting the rule that
says inserting a coin unlocks the gate. That
is the exact mechanism. He was mutating the logical
flowchart of the machine itself to see if a slightly
corrupted set of rules could predict a sequence
of numbers better than the original rules. That
is wild. But then you have John Henry Holland
at the University of Michigan around the same
time and his approach. genetic algorithms that
feels much closer to literal biology. Yeah, Holland
wanted to understand the actual mechanics of
adaptation. He was asking, how does a biological
organism physically pass down winning traits?
To simulate this, he represented data as chromosomes
using strings of binary bits, zeros and ones.
So if we visualize that, a single solution in
the computer's memory might look like the sequence
0, 1, 1, 0. And another solution might be 1001.
Exactly. And each position in that string represents
a specific trait. Holland's true genius was simulating
crossover. He wouldn't just mutate a single bit.
He would take those two parent strings, cut them
both in half, and swap their tails. Oh, wow.
So the first half of parent A gets glued to the
second half of parent B. creating a completely
novel offspring that inherits complex combinations
of traits from both parents. Furthermore, Holland
wasn't just working with one parent and one child
like Fogel. Holland created massive populations
of these bit strings and had them compete in
a giant ongoing tournament every single generation.
An absolute battle royale for binary code. It
is so fascinating that these brilliant minds
were all stumbling toward the exact same biological
truth from completely different angles. Fogel
mutating logic gates, Holland cross -breeding
data strings, Rechenberg mutating engineering
shapes. And they were totally separate until
the term evolutionary computing was finally coined
in 1991 to unite the field. Unification is huge.
It was. It allowed researchers to share concepts
across disciplines, and it paved the way for
the fourth major branch to emerge, which is perhaps
the most radical of them all. Here's where it
gets really interesting. In the 1990s, a researcher
named John Koza pushes the boundary with genetic
programming. Up until this point, researchers
were evolving data or evolving the parameters
of a solution. COSA decided to evolve the software
itself. Yes. The subject of the evolution was
a functional computer program written in a high
-level language called Lisp. Evolving actual
source code. Yeah. But wait, if you randomly
slice two pieces of computer code together, wouldn't
you just get a syntax error like... 99 % of the
time? You would think so, yes. Because if you
randomly delete a bracket or swap a variable
in traditional code, the entire program just
crashes and refuses to compile. Yeah. How does
the software survive long enough to evolve? Well,
that is the brilliant reason COSA used Lisp.
Lisp utilizes something called s -expressions,
which are structured as nested lists. You can
visualize them as tree branches. OK, tree branches.
The trunk of the tree is a mathematical operation,
like addition. The branches coming off that trunk
are the numbers being added. So instead of cutting
text blindly, like a word processor, the algorithm
is snapping off an entire functionally complete
branch of logic, say, a branch that calculates
a specific trajectory and plugging it into the
socket of another program's tree. Exactly. Because
it swaps whole functional branches, the grammar
of the code remains completely intact. The new
Frankenstein program might produce a terrible,
completely wrong answer, but it will successfully
run without a syntax error. That's incredible.
It allows the computer to cross -breed its own
instructions for sequence induction, pattern
recognition, and complex planning. Software that
writes and breeds better versions of itself.
I mean, it sounds like a science fiction utopia.
You just tell the computer what you want and
it breeds the perfect app. The literature actually
calls out a major modern problem in this field,
a dark side to all this biological inspiration.
Yeah, unfortunately. It's essentially academic
spam. It is a significant hurdle right now. Because
evolutionary computation is so deeply tied to
biological metaphors, researchers in recent years
have started proposing dubious novel algorithms
with incredibly poor experimental validation.
Yeah, there is an actual catalog keeping track
of this called the Evolutionary Computation Bestiary.
It sounds like a medieval book of monsters, but
it's really a list of bad code. It highlights
a really frustrating trend. Researchers will
take a highly successful mathematically proven
algorithm, frequently something called particle
swarm optimization. Oh, break down how the swarm
works. Particle swarm optimization mimics a flock
of birds searching for food. You have a massive
number of data points moving through a mathematical
space. Every single point remembers its own personal
best location. But it also constantly communicates
with the rest of the flock to know the absolute
best location found by the group so far. Right,
sharing intel. Exactly. Every point adjusts its
velocity and direction based on both its personal
memory and the flock's collective knowledge.
It's a gorgeous piece of math. The flock naturally
swoops in and converges on the perfect solution.
So what are these dubious researchers doing to
it? They are stealing that exact mathematical
engine, copying it line for line, and simply
slapping a brand new biological metaphor on top
of it. You're kidding. No, they swap the word
bird for bat or cuckoo bird or, you know, gray
wolf. They claim they have invented a revolutionary
new gray wolf optimizer, published a paper, and
just had their academic resumes. So they're looking
at a solved problem and saying, behold. I've
invented a new animal -themed algorithm. When
under the hood, the engine hasn't changed at
all. Does this mean the field has stalled out?
Like are we just out of actual foundational ideas?
Not at all. The copycats generate a lot of noise,
but the true science is accelerating. Foundational
evolutionary algorithms are legitimately being
used right now to solve multi -dimensional logistics.
They optimize the aerodynamics of jet engines
and route global telecommunications networks.
They are solving problems that are far too complex
for human software designers to map out manually.
The field hasn't stalled. It is simply dealing
with the growing pains of its own success. That's
reassuring. And while some researchers might
be faking new algorithms, the original algorithm
biological life itself is actually revealing
secrets we never expected, all thanks to this
computer science research. If we connect this
to the bigger picture, genetic algorithms have
completely revolutionized how we understand systems
biology. This was the absolute biggest mind bender
in the entire deep dive. The research suggests
that biological systems aren't just like computers.
It argues that biological systems are literal
computational machines. Yeah, for a very long
time we viewed biology through the lens of classical
dynamical systems. We assumed development was
orderly, well controlled, and governed by a top
-down structure. Like a central brain or a master
blueprint aggressively telling every single cell
exactly what to do and where to go. Right. But
studying evolutionary computation has proven
that there is no central control in biological
development. An embryo develops as a result of
purely local interactions. Just cell -to -cell?
Yes. A single cell doesn't know what the whole
body looks like. It is essentially taking chemical
inputs from its immediate neighbors, running
an algorithm, and computing its next state. The
cell is acting exactly like a logic gate in a
silicon chip. It is a literal low -level computational
operation. So what does this all mean? The documentation
states that if you apply concepts from computational
theory to biology, microprocesses in biological
organisms are fundamentally incomplete and undecidable.
Yes. Does that mean natural evolution operates
mathematically like an infinite, unpredictable
computer program? It does. This is proven through
a theoretical framework called evolutionary automata.
An automaton is essentially an abstract machine
that follows a set of rules to process input,
right? Like a Turing machine. A Turing machine
is a highly advanced automaton that can simulate
any step -by -step computer algorithm, but computer
scientists have generalized this into evolutionary
Turing machines. Okay. And the math proves that
natural evolution and the algorithms we run on
computers are mathematically undecidable. Meaning
you literally cannot write a formula to predict
the outcome of evolution. The only way to know
what an evolutionary system will do is to press
play, let it run, and watch what happens. It
is deeply unpredictable. And even more profoundly,
the simplest subclass of these theoretical machines,
evolutionary finite automata, can process and
accept incredibly complex arbitrary languages.
Languages in the mathematical sense? Yes, including
languages that are not recursively enumerable.
Okay, we need to translate that. What does it
mean for mathematical language to not be recursively
enumerable? Well, a standard traditional computer
can only solve problems that have a step -by
-step algorithmic solution. If you can list the
steps, the computer can solve it. Makes sense.
A non -recursively enumerable problem is one
where no step -by -step algorithm exists. A standard
computer will freeze or run forever trying to
solve it. but an evolutionary automaton can solve
it. Because it doesn't rely on step -by -step
logic. Exactly. It uses massive populations and
random mutations to explore every possibility
simultaneously. It confirms that nature is, mathematically
speaking, the ultimate supercomputer. It can
process information that our rigid, linear silicon
machines cannot even comprehend. It's staggering.
It really shifts how you look at the world around
you. Because for you listening to this right
now, whether you're trying to optimize a global
supply chain, or you're a developer trying to
train a massive new AI model, or honestly, if
you're just looking out the window at a tree,
you are witnessing the exact same fundamental
math at work. Nature's slow, brutal optimization
process is now the very thing designing our most
advanced technologies. This raises an important
question, though. A truly profound concept that
the research points toward by including universal
Darwinism in the broader implications of this
field. Universal Darwinism. We're expanding beyond
biology and computers entirely. Lay it on me.
Well, if evolutionary finite automata can process
these unimaginably complex, non -recursively
innumerable languages, and if we've proven that
both biological cells and silicon chips are really
just different types of hardware running the
exact same underlying evolutionary software,
Oh man, where are you going with this? Is the
entire universe itself just one giant ongoing
evolutionary computation? Is reality fundamentally
a massive algorithm trying to solve an unknown,
undecidable problem? Oh wow, that is the kind
of thought that fundamentally changes how you
view your own existence? Are we just the random
mutations trying to bounce the universe out of
a local minimum? Incredible. Well, we will leave
you to ponder that one on your own. Thank you
so much for joining us on this deep dive. Keep
questioning the world around you. And the next
time you use a piece of cutting edge tech, just
remember, it is probably running on Mother Nature's
stolen playbook.