BioXcelerator Technologies, LLC

Innovators | Ron Crawford, Terry Soule, Tom Hess
  

Life always finds a way. And apparently, so does simulated life.

Scientists at the University of Idaho recently developed a novel computer program that optimizes complex biological systems using nature’s most potent problem solver – evolution.

“If evolution solves problems in the wild, why not apply it to practical problems in the laboratory?” says Terry Soule, professor of computer science.

There are many biological processes in nature that scientists and engineers would like to optimize. For example: using bacteria to produce biofuels, maximizing the nutrient qualities of soil for particular crops or naturally neutralizing toxins in the ground water. Finding the best combination of multiple variables can get very complicated very quickly.

This is where the genetic algorithm comes into play.

The program selects a random set of solutions by taking all of the variables and combining them in a set of random solutions. Scientists test the solutions to see how well they work and put the data into the genetic algorithm.

The program then takes the best solutions and looks for common traits that might be important for an optimal solution. It combines these traits while introducing random mutations to create a new set of solutions. The whole process is repeated until the program evolves an effective combination.

The process allows scientists and engineers to optimize a complex set of variables without having to completely understand the processes or spend weeks – or even months – designing a model of the system.