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Algorithms in Nature

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Algorithms in Nature

Algorithms in ancient times

When you think of algorithms, you’re probably thinking of Wall Street supercomputers whizzing around making thousands of calculations and transactions in a fraction of a second or social media companies monitoring your every click and comment, targeting you with ads and polarizing content.

The concept of an algorithm however, has been in use by humans for thousands of years in various forms such as instructions for performing tasks, solving problems and making decisions. Ancient civilizations, such as the Greeks and the Babylonians, have used algorithms in their mathematical and construction practices. Euclid, a Greek mathematician, described the Euclidean algorithm for finding the greatest common divisor of two numbers in 300 BCE. Ancient Egyptians and Greeks used the “Ruler and Compass” construction algorithm. The Babylonians used an algorithm for performing multiplication and division using base 60.


It isn’t just humans that use algorithms to their benefit, nature itself uses algorithms to arrange and adjust ecosystems.

Examples of algorithms in nature

Through a large extent nature itself is organized through algorithms. In this article we’ll show you eight examples of algorithms in nature:

  1. The foraging behavior of ants, where they use a simple rule-based algorithm to find and collect food
  2. The flocking behavior of birds, where they use a decentralized algorithm to stay together and avoid collisions
  3. The swarm intelligence of bees, where they use a simple communication protocol to search for food and share information about the location of flowers
  4. The genetic algorithm of evolution, where organisms with advantageous traits are more likely to survive and reproduce, passing on those traits to their offspring.
  5. The navigation of sea turtles, which use the earth’s magnetic field to orient themselves and return to their nesting beach.
  6. The homing behavior of homing pigeons, which use a combination of visual landmarks, the sun’s position, and the earth’s magnetic field to find their way home.
  7. The growth patterns of plants, such as the Fibonacci sequence found in the arrangement of leaves and branches.
  8. The colony behavior of termites, where they use a decentralized algorithm to build and maintain their nests.

Foraging behaviour of ants

Ants are known for their efficient foraging behavior, which is the process of searching for and collecting food. They use a simple rule-based algorithm to find and collect food, which is based on two main principles: positive feedback and stigmergy.
Positive feedback refers to the process where ants leave a chemical trail, called a pheromone trail, behind as they travel to and from a food source. Other ants can then follow this trail to the food source. As more and more ants follow the trail, the pheromone concentration increases, making the trail more attractive to other ants. This creates a positive feedback loop, where the more ants that follow the trail, the stronger the trail becomes.
Stigmergy refers to the process where ants use the environment to communicate information about the location of food. For example, when an ant finds a food source, it will lay down a pheromone trail that other ants can follow. As more ants follow the trail, they will also lay down pheromones, making the trail stronger and more attractive to other ants. This allows ants to communicate information about the location of food without directly communicating with each other.
The combination of these two principles allows ants to efficiently find and collect food in a decentralized way. Without a central command or leader, the colony of ants is able to adapt to changes in the environment and find food sources quickly and efficiently.

The flocking behaviour of birds

The flocking behavior of birds is a well-studied phenomenon in which birds move in coordinated groups, also known as a flock. The algorithm that controls this behavior is based on a set of simple rules that govern the movement of individual birds in the flock. These rules are decentralized, meaning that they are based on local interactions between birds and do not require a central control or leader.

The three main rules that are used to govern flocking behavior are:

  • Separation: Each bird attempts to maintain a certain distance from its nearest neighbors to avoid collisions.
  • Alignment: Each bird aligns its direction of travel with that of its nearest neighbors.
  • Cohesion: Each bird moves towards the average position of its nearest neighbors, creating a cohesive group.

By following these simple rules, birds are able to move in coordinated groups, maintain a stable formation, and avoid collisions. Additionally, these rules allow flocks to respond quickly to changes in the environment, such as a potential predator or a new food source.

The swarm intelligence of bees

Bees use swarm intelligence, a type of collective behavior, to efficiently search for food and share information about the location of flowers. They employ a simple communication protocol, called the “waggle dance,” to accomplish this. When a bee discovers a new food source, it performs a figure-eight pattern dance. The angle and duration of this dance encodes information about the direction and distance of the food source. Other bees in the hive can then observe the dance, use the encoded information, and fly to the food source. Additionally, bees use other forms of communication such as pheromones, vibrations, and tactile communication to share information about the location of food. This decentralized approach allows the colony to adapt quickly to changes in the environment and find food sources efficiently.

The genetic algorithm of evolution

In the process of evolution, organisms with advantageous traits are more likely to survive and reproduce. This passing on of traits from parent to offspring is what is known as genetic algorithm. Natural selection changes a population’s genetic makeup over time, leading to new species. This algorithm favors characteristics that aid survival and reproduction to be passed on, and less favorable ones to be passed on less likely. This process gradually changes the genetic makeup of a population over time, leading to the development of new species.
Mutations, which are random changes in an organism’s DNA, also play a role in the genetic algorithm of evolution by introducing new genetic variations into a population. These mutations can lead to the development of new traits that may be beneficial for survival and reproduction.
The genetic algorithm of evolution is an active process that drives the diversity and adaptation of life forms on earth. It’s a powerful algorithm that has led to the development of millions of species over billions of years, shaping the diversity of life on our planet.

The navigation of sea turtles

Sea turtles use a complex navigation algorithm to orient themselves and return to their nesting beaches. The algorithm is based on the Earth’s magnetic field, which sea turtles are able to sense using specialized cells in their brains.
When sea turtles hatch from their eggs, they use this magnetic sense to orient themselves towards the sea. As they swim, they take note of the magnetic field of the earth and use it as a reference to navigate. This allows them to maintain a consistent heading while they swim and eventually return to the same nesting beach where they were born.
Scientists believe that sea turtles use a process called “magnetic map” which allows them to build a representation of the magnetic field of the earth in their brains. This map is based on the intensity and direction of the magnetic field at different locations, and allows sea turtles to use the earth’s magnetic field as a reference to navigate.
they also use other cues such as the sun’s position, wave direction, and smell to navigate.

The homing behavior of homing pigeons

Homing pigeons use a combination of algorithms to navigate and find their way back to their home loft. These algorithms include visual landmarks, the sun’s position, and the earth’s magnetic field.
When homing pigeons are trained to return to their home loft, they actively build a mental map of the surrounding area, taking note of visual landmarks such as roads, buildings, and other distinctive features. They also use the sun’s position to orient themselves and determine their direction of travel.
In addition to visual cues, homing pigeons, like sea turtles, also have the ability to sense the earth’s magnetic field. They use this ability to navigate by detecting changes in the magnetic field and using it as a reference to maintain a consistent heading.
The combination of these algorithms allows homing pigeons to navigate and find their way back to their home loft even when they are released in unfamiliar locations.

The growth patterns of plants

The growth patterns of plants are determined by algorithms that control cell division and differentiation. These algorithms are based on the genetic information stored in the plant’s DNA and the environmental cues that the plant receives.
One example of a growth pattern algorithm in plants is the Fibonacci sequence, which is found in the arrangement of leaves and branches. The Fibonacci sequence is a series of numbers where each number is the sum of the two preceding ones, starting from 0 and 1. This sequence can be observed in the arrangement of leaves and branches on a stem, where the number of branches at each level is the sum of the number of branches at the previous two levels.
Another example of a growth pattern algorithm in plants is phyllotaxis, which is the arrangement of leaves on a stem. The number of leaf primordia, precursor cells that develop into leaves, and the angle between them determine this pattern. Genes control this algorithm by regulating the formation of leaf primordia and timing their differentiation into mature leaves.
Plants also use other algorithms to control their growth, such as phototropism, where they sense the direction of light and grow towards it, and geotropism where they sense the direction of gravity and grow towards it.

The colony behavior of termites

The colony behavior of termites is based on a decentralized algorithm that allows them to build and maintain their nests. This algorithm is based on simple rules that govern the behavior of individual termites and their interactions with the environment.
One of the main rules that govern the colony behavior of termites is the division of labor. Each termite in the colony has a specific role such as foraging, caring for the young, or building and maintaining the nest. This division of labor allows the colony to function efficiently and effectively.
Another important rule that governs the colony behavior of termites is, like ants, the use of pheromones. Termites use pheromones to communicate with each other and coordinate their activities. For example, when a termite finds a food source, it will leave a pheromone trail that other termites can follow. This allows the colony to efficiently search for food and share information about the location of resources.
Additionally, termites also use other forms of communication such as vibrations, tactile signals and chemical cues to coordinate their activities.

Summing up algorithms in nature

Different organisms use various algorithms to adapt to their environment and optimize their survival. Ants use positive feedback and stigmergy, birds use separation, alignment and cohesion rules, bees use the “waggle dance” and other forms of communication, the genetic algorithm of evolution drives the diversity and adaptation of life forms, sea turtles use the Earth’s magnetic field for navigation, homing pigeons use visual landmarks, sun’s position, and the Earth’s magnetic field, plants use a combination of genetic information and environmental cues to control their growth patterns and termites use a decentralized algorithm based on simple rules to build and maintain their nests. All these examples demonstrate how organisms use algorithms to adapt and survive.

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