We will also detail all of the features available in Generator, and how these feature make Generator the most powerful product of its kind on the market. Some applications will also be discussed, including some performance comparisons with our nearest competitor. The program will run considerably faster if a later model computer with more memory is used. Genetic Algorithms Applications which use genetic algorithms to solve problems are surfacing in a wide range of areas. Over 500 research articles were published in 1994 alone on genetic algorithms, and widespread interest in these computer programs is increasing.
Genetic algorithms are essentially a software version of the evolutionary process.
The main difference is the computer process can be greatly accelerated by fine-tuning and manipulating the evolutionary principles, where members of the population are born, mate, and die in a few microseconds. This allows for enormous change to occur rapidly. Genetic algorithms require a set of population members , usually numbering between 20 to 100. Each population member represents a trial solution to a given problem. The inputs for the fitness function are often called genes , chromosomes, or genomes.