Know the principle of operation (algorithm) of the genetic algorithm approach ([2]: p. 99) Evolution Inside Your Computerthat you do not need to know how to solve a problem; you only need to know how toencode it in a way the genetic algorithm mechanism can utilize.Typically, the chromosomes are encoded as a series of binary bits. At the start of arun, you create a population of chromosomes, and each chromosome has its bits setat random. The length of the chromosome is usually fixed for the entire popula-tion. As an example, this is what a chromosome of length twenty may look like:0101001010010100||||The important thing is that each chromosome is encoded in such a way that thestring of bits may be decoded to represent a solution to the problem at hand. It may bea very poor solution, or it may be a perfect solution, but every single chromosomerepresents a possible solution (more on the encoding in a moment). Usually thestarting population is terrible, a little like the English cricket team or an Americanplaying football (sorry, soccer). Anyway, like I said, an initial population of randombits is created (let's say one hundred for this example), and then you do this (don'tworry about the italicized phrases. I'll be explaining each one in just a moment):Loop until a solution is found:1. Test each chromosome to see how good it is at solving the problem andassign a fitness score accordingly.2. Select two members from the current population. The probability of beingselected is proportional to the chromosome's fitness-the higher the fitness,the better the probability of being selected. A common method for this iscalled Roulette wheel selection.3. Dependent on the Crossover Rate, crossover the bits from each chosen chro-mosome at a randomly chosen point.4. Step through the chosen chromosome's bits and flip dependent on theMutation Rate.5. Repeat steps 2, 3, and 4 until a new population of one hundred members hasbeen created.End loopEach loop through the algorithm is called a generation (steps 1 through 5). I call theentire loop an epoch and will be referring to it as such in my text and code.What's Roulette Wheel Selection?Roulette wheel selection is a method of choosing members from the population ofchromosomes in a way that is proportional to their fitness-for example, the fitter99

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Know the principle of operation (algorithm) of the genetic algorithm approach ([2]: p. 99)

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Question

Know the principle of operation (algorithm) of the genetic algorithm approach ([2]: p. 99)

Evolution Inside Your Computer
that you do not need to know how to solve a problem; you only need to know how to
encode it in a way the genetic algorithm mechanism can utilize.
Typically, the chromosomes are encoded as a series of binary bits. At the start of a
run, you create a population of chromosomes, and each chromosome has its bits set
at random. The length of the chromosome is usually fixed for the entire popula-
tion. As an example, this is what a chromosome of length twenty may look like:
0101001010010100||||
The important thing is that each chromosome is encoded in such a way that the
string of bits may be decoded to represent a solution to the problem at hand. It may be
a very poor solution, or it may be a perfect solution, but every single chromosome
represents a possible solution (more on the encoding in a moment). Usually the
starting population is terrible, a little like the English cricket team or an American
playing football (sorry, soccer). Anyway, like I said, an initial population of random
bits is created (let's say one hundred for this example), and then you do this (don't
worry about the italicized phrases. I'll be explaining each one in just a moment):
Loop until a solution is found:
1. Test each chromosome to see how good it is at solving the problem and
assign a fitness score accordingly.
2. Select two members from the current population. The probability of being
selected is proportional to the chromosome's fitness-the higher the fitness,
the better the probability of being selected. A common method for this is
called Roulette wheel selection.
3. Dependent on the Crossover Rate, crossover the bits from each chosen chro-
mosome at a randomly chosen point.
4. Step through the chosen chromosome's bits and flip dependent on the
Mutation Rate.
5. Repeat steps 2, 3, and 4 until a new population of one hundred members has
been created.
End loop
Each loop through the algorithm is called a generation (steps 1 through 5). I call the
entire loop an epoch and will be referring to it as such in my text and code.
What's Roulette Wheel Selection?
Roulette wheel selection is a method of choosing members from the population of
chromosomes in a way that is proportional to their fitness-for example, the fitter
99

Process or set of rules that allow for the solving of specific, well-defined computational problems through a specific series of commands. This topic is fundamental in computer science, especially with regard to artificial intelligence, databases, graphics, networking, operating systems, and security.

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