In his new book, Science After Babel, David Berlinski expands on his explanation of the development and significance of algorithms, a subject he first examined in The Advent of the Algorithm. Berlinski writes, “The calculus and the rich body of mathematical analysis to which it gave rise made modern science possible, but it was the algorithm that made possible the modern world. The algorithm has come to occupy a central place in our imagination. It is the second great scientific idea of the West [after calculus].”¹

Algorithms have recently become a key in the development of artificial intelligence. Berlinski comments, “An algorithm is thus an ambidextrous artifact, residing at the heart of both artificial and human intelligence.” In addition to computer programs, Berlinski recognizes the role of algorithms in biology, writing, “The algorithm has made the fantastic and artificial world that many of us now inhabit. It also seems to have made much of the natural world, at least that part of it that is alive.” More specifically, regarding the fundamental organization of organisms he writes, “Molecular biology has revealed that whatever else it may be, a living creature is also a combinatorial system, its organization controlled by a strange, hidden, and obscure text, one written in a biochemical code. It is an algorithm that lies at the humming heart of life, ferrying information from one set of symbols (the nucleic acids) to another (the proteins).”

Examples of Animal Algorithms

A number of examples of algorithms in biology are described in my book Animal Algorithms.² The examples focus on particular types of programmed animal behaviors, including navigation, architecture, and social behaviors. They range from mathematical (navigation) to decision making (social colony behaviors) algorithms. It remains largely a mystery how such algorithms are programmed in the brains of animals, although generally it is assumed they are defined in various forms of neural networks. 

Despite the uncertainty in how such algorithms are embedded in animals, in the last several years there has been an interesting development in the field of biomimetics, which is the emulation of the design of systems in nature to help analyze and solve complex human problems. The basic idea is that since the animal behaviors are largely optimized, therefore it seems reasonable to attempt to mimic such behaviors in attempting to optimize analogous human applications. Computer algorithms have been developed based on animal behaviors that are algorithmic, including some described in Animal Algorithms.

Swarm Intelligence

The general discipline of such optimization algorithms is called Swarm Intelligence, which is “The characteristic of a system where agents interact locally with their environment so that their collective behaviors render the emergence of cohesive functional global patterns.”³ Particle swarm optimization is one of the more popular forms of such algorithms, which has been applied to a diverse array of applications, including automation control systems, communications, operations research, and energy management. One result of this is that there was a near exponential growth in the number of papers published on the subject early in this century.⁴

Other examples include ant colony optimization and bee colony algorithms. Ant colony optimization and bee algorithms are related to the foraging behavior of ants and bees. Ant colonies use a method of laying down pheromones on trails to enable other ants to follow. They tend to follow the strongest concentration of pheromone. Over time the group converges on the shortest path. It has been documented that many animals are capable of optimizing their routes when foraging for food. One application of ant colony optimization algorithms is with the so-called traveling salesman problem, which is the situation where multiple locations are to be visited and the challenge is to determine the optimum routes to minimize the distance travelled. An example would be optimization of the routes of Amazon delivery trucks. The performance of such algorithms was documented in one study of bee foraging, where the results indicated that 80 percent of the bees adopted an optimal sequence as their primary route. The conclusion of the study was that they “can find optimal solutions to dynamic traveling salesman problems simply by adjusting their routes by trial and error in response to environmental changes.”⁵

Wireless Network Routing

A study involving a bee colony optimization algorithm simulated the performance of wireless network routing.⁶The study demonstrated that asynchronous and decentralized routing decisions (as occur in bee colonies) can be optimized based on the algorithm, concluding that, “The nature inspired routing algorithm offers improved performance when compared to the existing state-of-the-art models.” Ant foraging algorithms have also been found to have an application related to the control of internet traffic.⁷

Berlinski writes, “Algorithms are human artifacts,” and therefore are also produced by intelligent agents.  What is amazing is that humans are continually trying to design systems that perform as well as some animal systems and behavior algorithms. While humans invented algorithms only within the last century, thus enabling the development of artificial intelligence, animals exhibit behavioral algorithms that long predate the existence of humans. This is another indication of the genius of biological systems and that they are the product of intelligent design.

Notes

1. David Berlinski, Science After Babel (Discovery Institute Press, Seattle, 2023, 41).
2. Eric Cassell, Animal Algorithms (Discovery Institute Press, Seattle, 2021).
3. Ahmen G. Gad, “Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review,” Archives of Computational Methods in Engineering, 2022, 29: 2531-2561.
4. Zhang, et al., “A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications,” Mathematical Problems in Engineering, Vol. 2015, ID 931256.
5.  “Travel Optimization by Foraging Bumblebees through Readjustments of Traplines after Discovery of New Feeding Locations,” Mathieu Lihoreau, Lars Chittka, and Nigel E. Raine, The American Naturalist, Vol. 176, No. 6, December 2010.
6. I. Jeena Jacob and P. Ebby Darney, “Artificial Bee Colony Optimization Algorithm for Enhancing Routing in Wireless Networks,” Journal of Artificial Intelligence and Capsule Networks, Vol. 03, No. 01, 2021.
7. Eric Cassell, “To Regulate Foraging, Harvester Ants Use a (Designed) Feedback Control Algorithm,” Evolution News, April 7, 2022.