- ○ Gene: a city in(x, y) coordinates
- ○ Individual (Chromosome): Each individual have chromosomes as permutation of number of cities. For example if the number of cities= 4, then [2,3,4,1] could be an example of an individual.
- ○ Population : a collection of possible routes (i.e., collection of individuals); It says how many chromosomes are in population. Depends on the type of encoding and the problem. If there are only few chromosomes, then GA would have a few possibilities to perform crossover and only a small part of search space is explored.If there are many chromosomes then GA slows down.
- ○ Parent : route which are combined to create new Route.
- ○ Mutation pool : .It may be defined as a small random tweak in the chromosome, to get a new offsprings. It is used to maintain and introduce diversity in our population and is usually applied with a low probability ( p m ) If the probability is high, the GA gets reduced to a random mutation.
- ○ Fitness: The fitness simply defined is a function which takes a candidate solution to the problem as input and produces as output how shortes and fit ,“optimal” the solution is with respect to the problem in consideration
- ○ Survival : It is the process of determining the individual that will proceed to next generation. Here we have used MU+Lambda, MU,Lambda or Replacement and random uniform.
Tirthnp/Solving-TSP-Using-Evolutionary-Algorithm
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