Evolutionary Computing Classification - Evolutionary Algorithms

Evolutionary Algorithms (EAs) and Swarm Optimization (SO) are algorithms used to solve optimization problems inspired by the principles of biological evolution. Even though they sound very similar, they have some key differences.

Evolutionary Algorithms (EA): EAs are a class of optimization algorithms inspired by the principles of biological evolution, such as natural selection and genetic recombination. They work by representing candidate solutions to a problem as a population of individuals and then using genetic operations such as selection, crossover, and mutation to evolve this population over time toward better solutions. Evolutionary Algorithms include families of algorithms like.

  1. Genetic Algorithms (GAs): Genetic algorithms use a genetic metaphor to evolve candidate solutions to a problem. They represent solutions as strings of binary or real-valued numbers and use genetic operations such as selection, crossover, and mutation to evolve these strings over time.
  2. Evolutionary Strategies (ESs): Evolutionary strategies use a probabilistic evolution model to evolve candidate solutions to a problem. They represent solutions as vectors of real-valued numbers and use perturbation operators to evolve these vectors over time.
  3. Evolutionary Programming (EP): Evolutionary programming uses an explicit evolution model to evolve candidate solutions to a problem. They represent solutions as functions and use genetic operations to evolve these functions over time.
  4. Genetic Programming (GP): Genetic programming uses a genetic metaphor to evolve computer programs to solve a problem. They represent programs as trees and use genetic operations such as crossover and mutation to evolve these trees over time.
  5. Differential Evolution (DE): Differential Evolution is a simple and efficient global optimization algorithm that uses differences between candidate solutions to drive the evolution.