Evolutionary Computation Framework

A technique for solving problems called evolutionary computation draws inspiration from the processes of natural evolution. It employs algorithms that mimic the adaptive systems found in nature to address complex optimization and search problem challenges.

Key Components of Evolutionary Computation

Population: A diverse set of possible solutions envisioned by individuals.

Fitness Function: A measure used to measure how well each member of a population solves a problem.

Genetic Operators: Genetic operators are the core components of genetic algorithms, a type of optimization algorithm inspired by the processes of evolution and natural adaptation. They are mechanisms that modify potential solutions proposed by individuals to create new, innovative, and improved solutions. While simulating mutation and reproduction processes, genetic operators help in exploring solutions and selecting the best optimal solutions.

Selection: The process of selecting individuals with high fitness for reproduction.

Termination Criteria: These are the factors and signs that stop the process of evolution.

How Evolutionary Computation Works

Initialization: Create a random population of individuals.