Question

Genetic Algorithm (GA) is one of Evolutionary Computation algorithms, which has found its wide applications in many areas. Explain the main steps involved in GA.

Answer 1

Genetic algorithms (GA) are global search and optimization methods based upon the principles of natural evolution and selection as developed by Darwin.

Genetic Algorithms are sufficiently randomized in nature, but they perform much better than random local search (in which we just try various random solutions, keeping track of the best so far), as they exploit historical information as well

Basic steps of genetic algorithm:
1)[Start] Generate random population of n chromosomes (suitable solutions for the problem)
2)[Fitness] Evaluate the fitness f(x) of each chromosome x in the population
3)[New population] Create a new population by repeating following steps until the new population is complete
   a)[Selection] Select two parent chromosomes from a population according to their fitness (the better fitness, the bigger chance to be selected)
   b)[Crossover] With a crossover probability cross over the parents to form new offspring (children). If no crossover was performed, offspring is the exact copy of parents.
   c)[Mutation] With a mutation probability mutate new offspring at each locus (position in chromosome).
   d)[Accepting] Place new offspring in the new population
4)[Replace] Use new generated population for a further run of the algorithm
5)[Test] If the end condition is satisfied, stop, and return the best solution in current population
6)[Loop] Go to step 2

Advantages of GA
1)Does not require any derivative information (which may not be available for many real-world problems).
2)Is faster and more efficient as compared to the traditional methods.
3)Has very good parallel capabilities.
4)Optimizes both continuous and discrete functions and also multi-objective problems.
5)Provides a list of “good” solutions and not just a single solution.
6)Always gets an answer to the problem, which gets better over the time.
7)Useful when the search space is very large and there are a large number of parameters involved.