What are Genetic Algorithms?
A. Genetic algorithms are general-purpose search algorithms based upon the principles of evolution observed in nature. Even with today’s high-powered computers, using an exhaustive search to find the optimal solution for even relatively small problems can be prohibitively expensive. For many problems, genetic algorithms can often find good solutions (near-optimal) in around 100 generations. This can be many times faster than an exhaustive search. For example, a chromosome containing 32 binary genes would have 4,294,967,296 possible combinations (solutions) to evaluate when using an exhaustive search. If the same problem were to be solved with a genetic algorithm of population size 50, requiring 100 generations of evolution, the genetic algorithm would only need to evaluate 5000 possible solutions. As mentioned previously, genetic algorithms are able to find optimal or near optimal solutions by using many of the principles of evolution that can be observed in nature. These include selec
Genetic Algorithms (GAs) are a general purpose problem solving technique. They simulate the evolutionary process of “survival of the fittest.” First, several random answers to a problem are generated. The worst answers are eliminated, and the best are “mutated” and “cross-pollinated” with each other to create additional answers that closely resemble the first. The repeated process of elimination and regeneration gradually improves the quality of answers. GAs are ideal for solving complicated problems with many independent (input) variables and a gigantic number of possible outcomes.
