The Evolutionary Search algorithm is an optimization method based on the mechanism of natural evolution. This algorithm simulates the evolution process of individuals within a population across generations to find the best solution for a problem.
How It Works
- Population Initialization: Create an initial population of randomly generated individuals.
- Evaluation: Evaluate the quality of each individual in the population based on the objective function or evaluation criteria.
- Selection: Select a subset of the best individuals from the current population based on probabilities or selection criteria.
- Evolution: Create a new generation by applying crossover and mutation operations to the selected individuals.
- Iteration: Repeat steps 2 to 4 over multiple generations until a satisfactory solution is achieved or a predefined number of iterations is reached.
Example: Optimizing the Fibonacci Function using Evolutionary Search
Consider the optimization problem of the Fibonacci function F(x) = F(x-1) + F(x-2) with F(0) = 0, F(1) = 1. We want to find the value of x for which F(x) is maximized. The Evolutionary Search method can generate a population of random x values, evolve them across generations to find the optimal x value.
Code Example in C++
In this example, we use the Evolutionary Search method to optimize the Fibonacci function. We generate a population of random x values, evolve them across generations by selecting the best individuals and applying crossover and mutation operations.