Genetic Algorithms

An algorithm is a set of rules to be followed in order to complete calculations and problem-solving applications. Most often associated with computer science and artificial intelligence, genetic algorithms (GAs) search for information from large sets of data to determine the best possible elements from within a data set. Therefore, genetic algorithms are search techniques based on the principles of the natural selection theory.

The natural selection theory is a theory where a species evolves through natural selection based on changes in the environment. Then, there is selective breeding based on desired or undesired traits. For example, in biology, plants are crossbreed based on their coding. White flowers blended with red flowers would eventually produce a pink flower based on dominant and recessive genes. This same type of theory of survival can be applied to business applications.

In the business world, the environment is constantly changing. This creates a difficult scenario for those who are working with the current market place from having to figure out every possible method that could go wrong with the market and running data. So, GAs are then used to go through the environment and discover demands and conditions in order to reach a business process that will most likely succeed in the current environment just like “survival of the fittest”.

Today, software programs are designed based on GAs to create market prediction, stock forecasting, financial trading, sales forecasting, and much more. These processes work because GAs discover elements within the data such as weaknesses, changes, strengths, and threats within an environment in order to search for solutions to problems. Business problems applicable for GAs include: information systems, marketing, finance, computer network design, job scheduling- hiring and firing, and tactical asset allocation.

Without GAs, a designer of a business process is left to try to decide all possible scenarios and changes in the environment in order to determine what could possibly happen in the future in order to naturally select changes for a business. GAs, however, create every possible and impossible scenario available; giving companies utilizing these systems an edge in competition. So, we have mentioned some possibilities in business and the environment where genetic algorithms are useful, but there are some that we view everyday and don’t even realize GAs are a part of the process.

Computer games, where a person can play against another play; whether online or not, is probably one of the most popular versions of genetic algorithms. Every possible scenario is predicted in the game to give expert players a more difficult play and beginners an easier time. In many, the AI learns from previous games in order to become more difficult to counter the human player’s strategies. When plotting for a trip or looking at an online map, one might discover quickest routes, longest routes, rush hour traffic and the like. These programs are probably designed by GAs and tracking routes use these to deliver packages and mail.

In a world where our environment and trends are constantly changing, it is difficult for businesses to keep track and remain current. One of the best benefits of genetic algorithms is they prepare prepare a company for what could possibly occur in the future. As mentioned previously, genetic algorithms are used in nearly every aspect of our lives from advertisements to the games we play. GAs are extremely adaptive and keep businesses, individuals, and society adaptable as well.