Random Particle Optimization

An implementation of a Genetic Algorithm in C. Genetic Algorithms, often abbreviated as GAs, are adaptive heuristic search algorithms inspired by natural selection and genetic evolution. They are commonly used to solve optimization and search problems by repeatedly improving a population of possible solutions over multiple generations.

This project is a practice implementation of a genetic algorithm and is designed to be easy to read, understand, and experiment with. The README explains the working of the algorithm in a very simple and beginner-friendly way, making it especially useful for students and developers who are learning how evolutionary algorithms work and how they can be implemented in software.

In this implementation, a gene is represented as a single character, while a genome is represented as a collection of characters. The program takes a target string as input and tries to evolve randomly generated genomes until one of them matches the target. Each genome is assigned a fitness score based on how closely it matches the target string. A perfect match has a fitness score of zero, while less accurate genomes receive lower scores.

The algorithm begins by creating two random parent genomes and a configurable number of offspring genomes. During each generation, the parent genomes are mated to produce offspring. Mating is performed using a single-point crossover, where part of one parent is combined with part of the other parent. A small random mutation may also be applied to prevent the search from getting stuck too early and to introduce variation into the population.

After the offspring are generated, their fitness values are calculated and the population is sorted. The two fittest offspring are selected as the new parents for the next generation. This process continues until the best genome converges to the target string or the iteration limit is reached.

The project is organized into separate modules for application initialization and genetic algorithm utilities. It also includes unit tests for important functions such as genome initialization, copying, mutation, mating, random number generation, sorting, and fitness calculation. This helps validate the algorithm implementation itself, not just demonstrate it.

Overall, this project is a compact and educational example of a genetic algorithm in C. It is useful for learning about evolutionary computation, optimization concepts, string evolution, randomness, fitness functions, and practical software implementation of algorithmic ideas.