Representation of a Solution in the Genetic Algorithm

Before we can start to use the genetic algorithm, we must answer the question What exactly is a population of solutions It is easy to envisage a population of crocodiles or ants or lamas, but what does a population of solutions look like  

Daisy = height at shoulder, weight, breed, predominant color, spot-ted( ), sex, average weekly milk yield, disposition...  

The vector of properties is a kind of chromosome, a code that defines the genotype of the individual. This sequence of values specifies the genetic makeup that (possibly uniquely) defines the animal. In Daisy s case, not all of the animal s characteristics are defined by her biological genetic code we have included in the vector other characteristics that would help to pick her out from the kine (cattle) crowd. In a similar way, a single GA solution can be constructed as a chromosome or genome in vector form, which contains all the information needed to define the potential solution to a scientific problem. 

The vector that the genetic algorithm manipulates is known conventionally as a chromosome or a string we shall use the latter terminology in this chapter. The individual units from which each string is constructed, a single x-, y-, or z-coordinate for an atom in this example, are referred to as genes. 

While floating-point values are used to construct the strings in most scientific applications, in some types of problem the format of the strings is more opaque. In the early development of the genetic algorithm, strings were formed almost exclusively out of binary digits, which for most types of problem are more difficult to interpret letters, symbols, or even virtual objects 

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