Genetic Algorithms - Snake Game
A genetic algorithm (GA) is a search heuristic inspired by the process of natural selection and genetics. It is commonly used to solve optimization and search problems. Here's how it typically works:
1. Initialization: Start with a randomly generated population of individuals (solutions). Each individual is represented as a chromosome, usually a string of binary values (though other encodings are possible).
2. Selection: Evaluate the fitness of each individual in the population. The fitness function measures how good a solution the individual is to the problem. Individuals with higher fitness are more likely to be selected for reproduction.
3. Crossover (Recombination): Select pairs of individuals (parents) from the current population and combine them to produce offspring. This is done by swapping parts of their chromosomes at random crossover points, simulating biological recombination.
4. Mutation: Apply random changes (mutations) to the offspring chromosomes with a certain probability. Mutation introduces genetic diversity, which helps prevent premature convergence to suboptimal solutions.
5. Replacement: Replace some or all of the old population with the new individuals (offspring) to form a new generation.
6. Iteration: Repeat the selection, crossover, mutation, and replacement steps for many generations until a stopping condition is met (e.g., a satisfactory fitness level is achieved or a maximum number of generations is reached).
7. Result: The best solution(s) found over the generations is taken as the output.
Genetic algorithms are particularly useful for complex problems where the search space is large and not well-understood, making traditional optimization techniques impractical. They are widely used in fields such as artificial intelligence, machine learning, engineering design, and economics.