genetic algorithm

Dutch family archives hold marriage records going back to 1600. From these records, like entries in an old ledger, you can extract two columns: how many children a person had and how many years they lived. Evolutionary biologists have been arguing for half a century over whether these columns are connected.

An old mattress stops bouncing back: the filling clumps up, and the springs lose their resilience. The ovary changes with age in much the same way, except that instead of filling, stiff connective tissue grows inside it, and the role of springs is played by the cells that support the eggs.

Picture a wind map over a mountain range. The wind mostly blows downhill, following elevation changes. But if you look closely, in some valleys the air swirls into little eddies that keep the wind from simply flowing down. That is exactly what the mutation field in human DNA looks like.

Two runners start a race on a course with changing incline. One starts earlier, the other later, and the judge measures their speeds without knowing about the staggered starts. So it is with virus variants: data arrive with delays, and if those delays are unequal, estimates of competitive advantage can be badly wrong.

A thousand candidates are sitting in the interview room, but only two hundred will be hired. It used to seem that to accurately estimate age from DNA, you had to interview every one of the thousand sites where a chemical mark alters gene activity. Now it's clear: you can hear out just a fifth of them, and the final decision barely changes.

A protein engineer is like a chef trying to make a dish that is tasty, healthy, and cheap all at once. Change one ingredient, and you risk ruining another. In the past, computer assistants could only evaluate protein properties one at a time, missing how they are connected.

Two photographers shoot the same mountain. One takes a hundred shots from the summit and only a couple from the base; the other does the opposite. If you try to align their images, the algorithm might decide that the summit in one photo matches the summit in the other simply because there are many similar points there, not because it's the same spot. Sampling density throws it off.

Hunting for a new material is like cooking without a recipe: you try, try again, and often throw things out. Previously, scientists manually flipped through papers and tested compositions one by one. Now there's a "robot chef" that reads the literature itself, mixes components, and checks what came out.

As you slowly turn the tuning knob of an old radio, the sound doesn't fade in gradually from the static: it bursts forth in a sudden jump, the moment the antenna hits resonance with the wave. Genes in a living cell behave much the same way.

The quantum world is like a kitchen where particles suddenly appear out of "nothing." But you need a chef — a classical field acting as an external source. Glauber's theorem, long known in optics, has now been generalized: it shows exactly how the field "cooks up" particles, be they photons or gravitons.

A huge orchestra without a conductor. Each musician is a gene, and the melody is the cell's complex work. Previously, looking at a tissue photo, a computer tried to guess each instrument's part individually. It didn't know that violins usually play with violas, and trumpets with trombones. The program just output a list of notes, without sensing the harmony.

The brain doesn't just react to the world: it's constantly guessing what will happen next. This principle is called predictive coding: neurons compare expectations with reality and update their internal picture when an error occurs. But how such a complex system could be wired into the living tissue of the cortex remained unclear.

A web where lions eat zebras and hyenas finish the leftovers is not just a list of dinners. It's a survival map for an entire region. When the climate shifts, animals move, and the map tears. To understand where it will tear and where it will hold, you need to find its unchanging 'skeleton'—the connections that repeat across different places.

The human mind is like a construction set that builds itself. New ideas don't die with their creators: they are passed on, mixed, and selected. This process is called cultural evolution: useful inventions stick, useless ones are forgotten. That's how a ratchet works — a mechanism that only turns forward. But what exactly happens in the head when one thought displaces another?

A protein slides along the DNA strand like a key along a keyring, searching for its lock. One wrong turn and a gene stays switched off. To know where a protein will grip firmly, you need to measure the strength of that grip — the binding energy.

Reconstructing a full picture from fragments is like discerning an orchestra's melody in a noisy hall. Such challenges arise in photometry, where a star's brightness is recovered from faint twinkling. Traditional methods need precise noise information, which is almost always unknown.

Imagine a cookbook that gets rewritten from generation to generation. One day, the borscht recipe receives a secret ingredient from a pastry chef, and from that moment, the taste remembers not only ancestors but also a neighbor. In the Standard Model of evolution, genes behave the same way.

A group of robot explorers is like a team of cooks tasting the same dish. Each samples their own portion and shares an impression: clear, occupied, or uncertain. But tastes are subjective — one might miss a chair leg, another might only lick the edge.

For a long time, biologists argued: if a herd of animals stably maintains a mix of aggressive and peaceful individuals, does each one alternately change behavior, or do some always fight while others always yield? In classical equations, these options are indistinguishable.

Genes are like houses in a city: in some, life bustles; in others, silence reigns. This 'urban' genetic landscape was analyzed in a thousand women with breast cancer.

The robot juggler catches and throws balls in precise rhythm with a human, like two пульсара sending pulses in unison. Juggling three balls is a classic cascade, governed by a steady beat, like the uniform rotation of a cosmic lighthouse. Break the rhythm and the pattern crumbles.

Data on gene activity usually looks like a long string of numbers, like a rainbow stripe (spectrum) with no visible connections. In the scVision model, these numbers are turned into a meaningful picture — not a list of ingredients, but a photo of a finished dish.

In the depths of the Universe, when a massive star collapses into a black hole or a magnetar is born, a gamma-ray burst flares up — for an instant, it outshines all the stars in the observable Universe.

In the depths of space, gravity is mastering the art of disguise. For a quarter century, astrophysicists have been trying to decipher the nature of dark energy, which makes the Universe expand at an accelerating rate.

A robot swarm is like a cloud of fireflies: each moves on instinct to wherever is freer, and chaos turns into order. For a long time, it was a mystery how coherence emerges without communication or commands. The answer came with the creation of the Response Map — a tool that, like an X-ray, illuminates the hidden logic of the machine brain.

A cook who learns to cook from hand-drawn pictures risks ruining every dish. The same trap awaits computer vision: models need thousands of labeled images, but collecting them manually is expensive and tedious. Synthetic frames solve the problem, but only if they look real.

A large neural network like Whisper masters hundreds of languages and resembles the standard model of the universe: everything is balanced. But training on a rare language disrupts this equilibrium — it turns into a black hole, pulling in all training resources. The network starts forgetting the other languages.

Molecules float in a pot of broth. Some copy themselves, others help their neighbors, and still others just get in the way. This isn't soup: it's a model of early life: a replicator system. Usually, we assume the rules of the game are fixed once and for all: whoever reproduces fastest wins. But the authors of this study proposed something different — the rules can change too, just very slowly.

DNA methylation consists of tiny chemical marks on our genes. Over the years, they change on a fairly strict schedule. Standard epigenetic clocks look at these marks and output a single number: biological age. But a number can't tell you how the pattern of marks rearranged day by day, year by year. The authors of this work decided to turn a static photograph into a continuous film.

Recommendation systems that decide which post you’ll see next run on deep neural networks made up of thousands of operations — intricate computation Graphs (blueprints of operations on data). These systems are living, ever-evolving Dynamical Systems: the slightest architecture change cascades through the entire graph.

A robot in a maze walks into a dead end. A minute later, it’s the first to find the exit. This illogical maneuver is exploration, not a mistake. Like a cartographer circling a mountain to see its slopes. Evolutionary algorithms in silico have long mimicked selection and mutation to search for the best solutions.

60 mice, 1,980 samples, 234 lung cancer patients. Behind each number lie thousands of genes. Their activity is measured to understand the disease. But the data aren’t uniform: for one patient, you assess a protein level (a continuous number), make a diagnosis (yes/no), and determine the tumor subtype (a category).

A cell is like a library where thousands of books are copied every day. Molecular machines, polymerases and ribosomes, slide along a template chain and assemble a copy, link by link. These machines have several states: they can slow down, make mistakes, or switch. Previously, calculating such a process took hours.

A person reaches out to a stranger across town. That stranger will never return the favor: they'll never meet. So why help? Biologists call this indirect reciprocity: kindness comes back not from the person you helped, but from someone else, down the line. In a new study, two variants of this idea were pitted against each other.

The immune system is like a tireless editor, revising antibody drafts to make them stickier to viruses and bacteria. This process is called antibody maturation: random typos (mutations) arise in the genes, and natural selection keeps those that improve target capture.

A hefty multi-volume reference book is the human reference genome. And the short sentence fragments scattered across the table are millions of sequenced DNA reads. The job of a navigator like BWA-MEM is to find the exact spot in the reference for each fragment.

An engineer tunes an antenna: pull one segment, the frequency drifts; change the angle, the gain drops. Developing therapeutic peptides (short protein chains) works much the same way.

A neural network, like a sheet of graph paper, receives homogeneous signals at its input, and draws its own coordinate grid. Initially, connections between inputs and network cells form a gentle hill: the response is strongest at the center and fades toward the edges.

You buy a new kitchen scale, but instead of exact weight, it only shows deviations from some "norm." To find out how much an apple weighs, you first have to weigh dozens of other apples and calculate the average.

Many cutting-edge physics calculations hit a brick wall — not for lack of ideas, but because of arithmetic. We're talking about integrals that express particle collision probabilities or the shape of gravitational wave bursts.