Dr. Noor Awad

Evolutionary Algorithms (EAs) are model-free population-based methods which generally include mechanisms inspired by nature (i.e. concepts in Darwinian Evolution) and solve problems through processes that emulate the behaviors of living organisms. EAs consist of  a method of initializing a population, mutation, crossover, selection operations, and a notion of fitness. The mix of potential solutions to a problem is populated randomly first. Then the population is tested for fitness -- how well and how quickly it solves a problem. The fittest individuals are then selected for reproduction through mutation and crossover operations. The cycle begins again as the fitness of the population is evaluated and the least fit individuals are eliminated. EAs are excellent at optimizing solutions to problems that cannot be solved easily using other techniques, and seemingly a simple EA can often solve complex problems. It is important to note though that while EAs optimize effectively, they don’t necessarily find the optimal solution. EAs have been known for black-box optimization and successfully used to solve many real-world applications in engineering, economics, bioinformatics, robotics and many others.