What is genetic algorithm and how is it applied in AI?

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A Genetic Algorithm (GA) is an optimization and search technique in AI inspired by natural selection and genetics. It is used to find approximate solutions to complex problems where traditional methods are inefficient.

How it works:

1. Initialization – Start with a population of possible solutions (chromosomes).

2. Fitness Function – Evaluate how good each solution is.

3. Selection – Pick the fittest individuals to reproduce.

4. Crossover – Combine parts of two solutions to create new offspring.

5. Mutation – Randomly alter some genes to maintain diversity.

6. Replacement – Form a new population and repeat until a stopping condition is met.

Over generations, the population evolves toward better solutions, just like survival of the fittest in nature.

Applications in AI:

• Optimization problems – e.g., scheduling, routing, resource allocation.

• Machine learning – feature selection, neural network weight optimization.

• Game AI – evolving strategies for non-player characters.

• Robotics – path planning and control optimization.

• Design problems – circuit design, engineering solutions.

Example: In a traveling salesman problem, GA can evolve paths over generations to minimize total distance, even when exact computation is infeasible.

👉 In short, Genetic Algorithms apply biological evolution principles to AI, making them powerful for solving complex, non-linear, and large search space problems.

Read More:

What is minimax algorithm in game-playing AI?

What is A* search, and how is it different from Dijkstra’s algorithm?

Explain breadth-first search (BFS) and depth-first search (DFS) in AI.

How does alpha-beta pruning improve minimax efficiency?

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