Is there a difference between genetic algorithms and evolutionary algorithms?
I have read multiple papers, talking about genetic or evolutionary algorithms, and while very similar, I think they may not be the same thing.
Evolutionary programming mainly uses mutation. Evolutionary programming is one of the four major evolutionary algorithm paradigms. It is similar to genetic programming, but the structure of the program to be optimized is fixed, while its numerical parameters are allowed to evolve.
Genetic Algorithms(GAs) are adaptive heuristic search algorithms that belong to the larger part of evolutionary algorithms. Genetic algorithms are based on the ideas of natural selection and genetics.
In the evolutionary computation domain, we can mention the following main algorithms: the genetic algorithm (GA) [1], genetic programming (GP) [2], differential evolution (DE) [3], the evolution strategy (ES) [4], and evolutionary programming (EP) [5].
An evolutionary algorithm (EA) is an algorithm that uses mechanisms inspired by nature and solves problems through processes that emulate the behaviors of living organisms. EA is a component of both evolutionary computing and bio-inspired computing. EAs are inspired by the concepts in Darwinian Evolution.
A genetic algorithm is a class of evolutionary algorithm. Although genetic algorithms are the most frequently encountered type of evolutionary algorithm, there are other types, such as Evolution Strategy. So, evolutionary algorithms encompass genetic algorithms, and more.
Genetic algorithms use crossover (hence the 'gene' in their name) and mutation to search the space of possible solutions.
Evolutionary programming uses primarily mutation.
As posted already, both are types of evolutionary algorithms.
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With