Fitness / fitness function / fitness landscape

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Advanced programming[1]

A fitness function is a particular type of objective function that is used to summarise, as a single figure of merit, how close a given design solution is to achieving the set aims. The function takes a candidate solution to the problem as input and produces as output how “fit” our how “good” the solution is with respect to the problem in consideration.[2]

Requirements for a fitness function:

  1. The fitness function should be clearly defined. The reader should be able to clearly understand how the fitness score is calculated.
  2. The fitness function should be implemented efficiently. If the fitness function becomes the bottleneck of the algorithm, then the overall efficiency of the genetic algorithm will be reduced.
  3. The fitness function should quantitatively measure how to fit a given solution is in solving the problem.
  4. The fitness function should generate intuitive results. The best/worst candidates should have best/worst score values.



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