Niket Girdhar

GAs are a class of optimization algorithms which are based on genetics and the process of natural selection. These are also known as Evolutionary Algorithms and generally generate high quality solutions.

Key terms/concepts in GAs:

Population: A set of potential solutions to a problem. This set tends to evolve over iterations also called generations.
Chromosome: Each individual function is called a chromosome and are often encoded as a string of bits, numbers or characters.
Fitness function: A function that helps evaluate and assign a fitness score to each chromosome based on how fit the solution is for the problem.
Selection: It is the process of choosing chromosomes from the current population set to create a new set for the next iteration of algorithm. Commonly used selection methods are tournament selection, roulette wheel selection and rank-based selection.
Crossover: It is also known as recombination and is an operator that combines two parent chromosomes to produce child chromosomes. Types of crossover include single-point, multi-point and uniform crossover.
Mutation: An operator that introduces minor random changes to the chromosomes. It helps the algorithm to prevent getting stuck with a optimized solution in just one set and not the overall optimized solution.
Termination Condition: Condition that checks if the solution satisfies the objective i.e. is optimized.
Generation: Each cycle of GA which involves the process of selection, crossover and mutation to produce a new population is called generation.

Applications:

Optimization problems like Travelling Salesperson Problem.
Machine Learning
Finding design optimization based on the entered specifics
In drug discovery, genetic alignment and other biology fields
In evolving AI behaviors

Advantages of GA:

Real-life data input is easier in GAs as compared to some of its competitor algorithms
Its implementation is quite easy
It is fast as compared to most optimization algorithms out there