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. In Mathematics, the term optimization, or mathematical programming, refers to the study of problems in which one seeks to minimize or maximize a real function In Genetic algorithms a chromosome (also sometimes called a genome) is a set of parameters which define a proposed solution to the problem that the genetic algorithm A genetic algorithm (GA is a Search technique used in Computing to find exact or Approximate solutions to optimization and Search 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. In Genetic algorithms crossover is a Genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next
Another way of looking at fitness functions is in terms of a fitness landscape, which shows the fitness for each possible chromosome. In Evolutionary biology, fitness landscapes or adaptive landscapes are used to visualize the relationship between Genotypes (or Phenotypes and
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.
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). Interactive evolutionary computation (IEC or Aesthetic Selection is a general term for methods of Evolutionary computation that use human evaluation
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