Effective Implementation
GAs are very general in nature, and just applying them to any optimization problem wouldn’t give good results. In this section, we describe a few points which would help and assist a GA designer or GA implementer in their work.
Introduce problem-specific domain knowledge
It has been observed that the more problem-specific domain knowledge we incorporate into the GA; the better objective values we get. Adding problem specific information can be done by either using problem specific crossover or mutation operators, custom representations, etc.
The following image shows Michalewicz’s (1990) view of the EA −
Reduce Crowding
Crowding happens when a highly fit chromosome gets to reproduce a lot, and in a few generations, the entire population is filled with similar solutions having similar fitness. This reduces diversity which is a very crucial element to ensure the success of a GA. There are numerous ways to limit crowding. Some of them are −
Mutation to introduce diversity.
Switching to rank selection and tournament selection which have more selection pressure than fitness proportionate selection for individuals with similar fitness.
Fitness Sharing − In this an individual’s fitness is reduced if the population already contains similar individuals.
Randomization Helps!
It has been experimentally observed that the best solutions are driven by randomized chromosomes as they impart diversity to the population. The GA implementer should be careful to keep sufficient amount of randomization and diversity in the population for the best results.
Hybridize GA with Local Search
Local search refers to checking the solutions in the neighborhood of a given solution to look for better objective values.
It may be sometimes useful to hybridize the GA with local search. The following image shows the various places in which local search can be introduced in a GA.
Variation of parameters and techniques
In genetic algorithms, there is no “one size fits all” or a magic formula which works for all problems. Even after the initial GA is ready, it takes a lot of time and effort to play around with the parameters like population size, mutation and crossover probability etc. to find the ones which suit the particular problem.
