A fitness function is a particular type of objective function that quantifies the optimality of a solution (that is, a chromosome) in a genetic algorithm so that that particular chromosome may be ranked against all the other chromosomes. Optimal chromosomes, or at least chromosomes which are more optimal, are allowed to breed and mix their datasets by any of several techniques, producing a new generation that will (hopefully) be even better.
An ideal fitness function correlates closely with the algorithm's goal, and yet may be computed quickly. Speed of execution is very important, as a typical genetic algorithm must be iterated many, many times in order to produce a usable result for a non-trivial problem and this is one of the main drawbacks of GAs in real world applications. This limits their applicability in some industries. It is apparent that amalgamation of approximate models may be one of the most promising approaches especially in the following cases:
- Fitness computation time of a single solution is extremely high,
- Precise model for fitness computation is missing,
- The fitness function is uncertain or noisy.
Another way of looking at fitness functions is in terms of a fitness landscape, which shows the fitness for each possible chromosome.
Definition of the fitness function is not straightforward in many cases and often is performed iteratively if the fittest solutions produced by GA are not what is desired. In some cases, it is very hard or impossible to come up even with a guess of what fitness function definition might be. Interactive genetic algorithms address this difficulty by outsourcing evaluation to external agents (normally humans).
References
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- An Nice Introduction to Adaptive Fuzzy Fitness Granulation (AFFG) (PDF), A promising approach to accelerate the convergence rate of EAs. Available as a free PDF.
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