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Fitness function - canonical genetic algorithm:
Conversely the fitness function will use an evaluation function to calculate a value of worth for the individual accordingly that they can be compared against each other. Frequently the evaluation function is written g(c) for a particular individual c. hence correctly specifying such evaluation functions is a risky job that we look at later. However the fitness of an individual is calculated through dividing the value it gets for g with the average value for g over the entire population as:
Here fitness(c) = g(c)/(average of g over the entire population)
Therefore we see that every individual has at least a chance of going with the intermediate population unless they score zero for the evaluation function. In fact as an example of a fitness function requiring an evaluation function, assume our GA agent has calculated the evaluation function for every member of the population plus the average is 17. After then, for a particular individual c0, the value of the evaluation function is 25. Moreover the fitness function for c0 would be calculated as 25/17 = 1.47. Because, one copy of c0 will definitely be added to the IP plus one copy will be added with a probability of 0.47 as in e.g., a 100 side dice is thrown and only if it returns 47 or less so there is another copy of c0 added to the IP.
Overriding tell us only the methods, but shadowing tells us the entire element.
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