Evolutionary algorithms are inspired by nature’s way of solving problems. These methods mimic biological concepts such as mutation, reproduction, recombination, selection, and inheritance. Over time, these operations allow populations to evolve towards optimal solutions.
We will explore various types of evolutionary algorithms, along with practical implementations in Python.
Section 1: Understanding Genetic Algorithms
Genetic algorithms are search heuristics based on principles of genetics and natural selection. They involve encoding potential solutions as chromosomes within a population, evaluating each individual according to its fitness score, then selecting individuals to reproduce offspring through crossover and mutation operations.
Let us start implementing a simple GA in Python for finding maxima of the function f(x)=x²:
- Define the objective function and initialize the initial generation:
import random
def func(x)…