Fitness Function in Genetic Algorithm

Fitness function in a genetic algorithm is also known as the evaluation function. It performs the role of evaluating how closely knit a solution is proffered to the maximum solution required for a particular problem. That is, if from a total number of 500, a 367 fitness search is recorded from across a generation, it’s most likely that this figure of 365 would be selected for further experiments on fitness. This means that such a population of persons would be picked for the genetic algorithm system to improve on the offspring for the coming generation.

The fitness function examines how to find a solution fit for a problem. However, to understand fitness function in a genetic algorithm, it is important to consider what genetic algorithm means.

Following the theory of Charles Darwin on natural evolution, the genetic algorithm presents an experimental heuristic search technique to reflect on the procedures of natural selection. One of the most recognized resolutions of the natural selection process is the “survival of the fittest” which means that only the fittest persons are selected for reproduction in producing newer and stronger offspring for the next generation.

For the genetic algorithm, five stages are considered for the trial and error experiment. The natural selection procedure begins with the selection of fittest persons from the pool of society’s population. These fittest individuals perform the reproductory functions for offspring with the inheritance of parents’ characters and strength or fitness which are then incorporated into the genetics of the next generation.

The process is to present society with fittest individuals. This equals to the logic that if the parents are fit, they will produce offspring that are fit for the next generations until the fittest individual in a particular generation is found as provided by the natural selection and evolution theory. This is also based on the discretion of the scientist who is interested in a genetic transformation or experiment.

To understand the genetic algorithm, it’s important to know the stages at which it operates.

Stages of a Genetic Algorithm

Initial Population: this is the pool where individuals exist. Without the population, there is no potential solution to the problem of finding the fittest to survive the times of development. Individuals are characterized by their specific genes and they are incorporated into a string to give a solution in the form of a chromosome.

The fitness function or evaluation function is the second stage. It is the determinant of the ability of probably selected individuals to compete in the survival of the fittest. The selection for reproduction is then based on the fitness score which is primarily centered on their performances and biological reaction to instructions.

Selection is the third stage of the genetic algorithm. This stage suggests the selection of fittest individuals to permit reproduction for the next generation.

Cross Over: this is regarded as a very critical stage in the genetic algorithm. It provides that before each pair of parents can mate together, a crossover point is randomly chosen within their genetics. This stage also features the production of offspring and the exchange of genes within parents until the crossover point is reached.

Mutation: This is the last stage of the genetic algorithm. It focuses on the formed offspring which often occur to sustain differences within the number of offspring available. The stage is also initiated to avoid premature convergence.

With this bit of introduction into the genetic algorithm, it should be emphasized that there is a fixed community of individuals. When the population is huge, those with the least fitness do not survive the next generation and the sequence of the stages is repeated to have individuals who are fit and stronger than the people of the previous generations.

As already established in the above, the genetic algorithm deals with the natural selection of species, the reproduction part, and the continuation of the cycle. However, why is fitness function important to this system of reproduction and transformation?

Fitness function examines a given solution and its provision of success into the quest for the problem of generations and population poses. The solutions in the genetic algorithm are represented by chromosomes. The chromosomes are then taken through an experimental stage and the score of this stage is given based on their performance.

The score is essential. This is because the observation of the potential solution provided suggests whether the individual will be selected or otherwise. This score is regarded as the fitness score. At this, there are a few important features that must be found in a potential solution for fitness function and they include:

A well-detailed fitness function: this means that the test shouldn’t just be carried out by masters in the field, a detailed report must be presented to aid the understanding of the considerations for awarding such fitness score for an experiment.

Efficient implementation of the fitness functions: for the solution to treat the problem that genetic algorithm is posed with, an adequate evaluation must be emphasized because poor conduct of the fitness function test would reduce the efficiency of the genetic algorithm and the result expected at the end of it all.

It should be noteworthy that potential solutions’ distinct ability in the interest of reproduction must be clearly stated too.

The fitness function must stick to the scores, untransformed. By this, the next stage in the genetic algorithm will quickly end.

It is believed that every problem has its evaluation function and its application is dependent on the kind of problem posed. However, how to find a fitness function to solve problems is a hard nut to crack in formulating a problem through the use of the genetic algorithm.

However, scientists have adopted some functions to shoulder the problems posed and for classification tasks, error measure software like the Manhattan distance and Euclidean distance is applied. For the challenges posed by optimizations, basic functions in the set of a calculated parameter are applied.