Genetic Algorithm [GA]

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