So far, we have seen chromosomes that represented binary or integer parameters. Consequently, the genetic operators were suitable for working on these types of chromosomes. However, we often encounter problems where the solution space is continuous. In other words, the individuals are made up of real (floating-point) numbers.
Historically, genetic algorithms used binary strings to represent integers as well as real numbers, however, this was not ideal. The precision of a real number represented using a binary string is limited by the length of the string (number of bits). Since we need to determine this length in advance, we may end up with binary strings that are too short, resulting in insufficient precision, or are overly long.
Moreover, when a binary string is used to represent a number, the significance of each bit varies by its location—...
