Genetic optimization uses programming algorithms to find the best solution to difficult nonlinear problems by mimicking biology’s evolution process. This approach requires significant computing power and can be used in various contexts, including securities trading and game programming.
Genetic optimization is the use of programming algorithms to find the best solution to a problem. This has its origins in the work of mathematicians starting in the 1950s who took models they saw in biology and applied them to nonlinear problems that were difficult to solve by conventional means. The idea is to mimic biology, which evolves over generations to create the fittest population possible. In programming, you can simulate this process to come up with a creative solution to a problem.
Nonlinear problems can be difficult for mathematicians. An example can be seen in securities trading, where there may be a number of possible decisions branching off rapidly to create a tree of choices. Independently calculating the probabilities associated with each choice would be time consuming. The mathematician could also miss an optimal solution by failing to combine possible choices to explore new permutations. Genetic optimization allows researchers to perform calculations of this nature more efficiently.
The researcher begins with a topic of interest, known as a “population,” which can be divided into individuals, sometimes known as creatures, organisms, or chromosomes. These terms, borrowed from biology, reflect the origins of this approach to programming. A computer can start running a simulation with population, selecting individual organisms within a generation and allowing them to mix to make a new generation. This process can be repeated through several generations to combine and recombine possible solutions, ideally arriving at the most suitable option for the given conditions.
This can be extremely resource intensive. The calculations used in genetic optimization require significant computing power to quickly compare and select a number of options and combinations at once. Early genetic optimization research was sometimes limited by the processing power available, as researchers could see potential applications, but could not run complex programs. As computer power increases, so does the usefulness of this method, although large and complex calculations may still require a highly specialized computer.
Mathematical researchers can work with genetic optimization in a variety of contexts. The ongoing development of new formulas and approaches illustrates the evolutions in mathematics as people learn new ways of looking at complex problems. Some simple genetic optimizations can be seen at work in environments such as software for stock traders and game programming and virtual reality where programmers want users to have a more natural experience.
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