How Artificial intelligence and Genetic Algorithm can enhance strategic decision-making in business.
Natural selection is considered as one of the most important theories in biology. It puts into perspective that every living organism, from the smallest bacteria to the human being, evolves through a series of beneficial mutations when adapted to their environments — changes in the genetic code that give it some advantages. Moreover, when these advantages are compounded, they grow through the population until an entirely new species exist, becoming a new and improved version of its ancestor. And just as this theory drastically changed the way we look at biology, its counterpart has the potential to revolutionize business strategy in the realm of artificial intelligence and competitive advantage in enterprise management. But how?
The Origin
Inspired by Darwin’s theory of natural selection, the Genetic Algorithm was developed as a means of modeling evolution and adaptation in multiple disciplines and has now found a number of applications in artificial intelligence. Darwin’s initial insight that led to his theory of evolution was realizing that there was always a variation of traits in a population. These traits competed with each other in their environment, fighting in some way for food and mates to have offspring. The trait more suited to the environment or preferred by the population would be selected, ensuring that the next generation would have more of it and the next even more until it becomes the dominant trait.
The Link Between Artificial Intelligence and the Genetic Algorithm
Applying this to artificial intelligence, researchers had the insight to create variations in an AI model and set them all on the same task. This variation then led to some AIs being more “fit” than others at the task, which were then selected to make more variations for the next generation. This process allowed researchers to find the models best suited to a task, eventually creating a far stronger model in a few generations of selection. Implementing this will enable systems to evolve and develop on their own, often in unprecedented ways that use methods entirely different from what humans have come up with. Furthermore, because these systems develop independently, they are not too influenced by how things are currently being done, as human researchers are prone to doing. As a result, these methods are unique, but at the same time, they can end up being more efficient and accurate, delivering better results than we could have hoped for.
These self-evolving methods have been used in a number of systems, from Boeing aircraft and NASA antennas to artwork and movie development. All these applications have one thing in common: they involve an exponentially high number of iterations that you would have to go through to find the best solution, which would take years for one person to try. But with evolving AI streamlining the process, it is completed much quicker and can be implemented.
Gaining a Competitive Advantage
In the context of enterprise management and business applications, I decided to put this concept into practice whenever we engaged with clients that had complex decision-modeling or recursive analysis. For example, we applied this algorithm in areas like decision-making during strategic planning. We gave the program hundreds of independent tasks, then the Algorithm helped prioritize them to achieve the best implementation, speed, and payoff, with the lowest risk.
Another major area we were able to successfully apply the Genetic Algorithm was in optionality and design thinking. This process was optimized to evaluate a new generation of products that would likely have the biggest success in the markets. To our surprise, the algorithm gave us substantially different results from what we came up with as a team of experts after a long Business Model Hackathon day. We were then able to select better options with this Algorithm because of unnoticed possibilities that we wouldn’t have been able to see without the Algorithm.
Artificial intelligence, combined with the evolutionary power of the Genetic Algorithm, has the potential to drastically improve several aspects of enterprise management and help businesses gain a competitive advantage. This experience opened my eyes to the realm of artificial intelligence applications in business and how tools like these can be deployed in business to optimize strategic choices and can be leveraged to augment management decision-making in enterprises to a whole new level.
When it comes to business and performance, we can learn a lot from nature. Just as Darwin’s evolutionary process inspired the Genetic Algorithm, research into animal flocking behavior and the complex systems they generate, inspired other business applications. And if you like what you learned here, check out my other article to learn more:
3 Rules from Nature’s Complex Systems for Better Business Performance
References
Edge, A. (2020, July 9). Antifragility: Embracing Reality and Ensuring Aliveness. Medium. https://antifragilityedge.medium.com/antifragility-embracing-reality-and-ensuring-aliveness-7d4aae23ae33
Katoch, S., Chauhan, S. S., & Kumar, V. (2020). A review on genetic Algorithm: past, present, and future. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-020-10139-6
Osterloff, E. (2019). What is natural selection? Natural History Museum. https://www.nhm.ac.uk/discover/what-is-natural-selection.html
Yang, X.-S. (2021). Genetic Algorithm — an overview | ScienceDirect Topics. Www.sciencedirect.com. https://www.sciencedirect.com/topics/engineering/genetic-algorithm