Understanding Genetic Algorithms
- Genetic Algorithms (GAs) are a type of optimization technique inspired by the principles of natural selection and evolution.
- They are particularly effective for solving complex problems where traditional methods may struggle.
Think of GAs as “survival of the fittest” applied to problem-solving.
- Describing GAs as purely random.
- They use randomness, but the process is guided by fitness evaluation.
Key Components of Genetic Algorithms
- Population: Initial set of potential solutions.
- Fitness Function: Evaluates how good a solution is (objective function).
- Selection: Chooses the best (fittest) solutions to reproduce.
- Crossover (Recombination): Combines parts of two solutions to create offspring.
- Mutation: Randomly alters parts of a solution to maintain diversity.
- Evaluation: Tests the new solutions using the fitness function.
- Termination: Stops when a solution is “good enough” or a condition is met (e.g., max generations).
- Mutation = exploration
- Crossover = refinement.
Forgetting that mutation is essential to avoid premature convergence (getting stuck in local optima).
Real-World Applications of Genetic Algorithms
Route Planning: The Travelling Salesperson Problem (TSP)
- Problem: Find the shortest route to visit a list of cities and return to the starting point.
- How GAs Solve It:
- Representation: Each route is a sequence of city indices.
- Fitness Function: Inverse of the total distance (shorter routes have higher fitness).
- Crossover: Combines parts of two routes while ensuring no city is visited twice.
- Mutation: Swaps two cities to explore new routes.
In logistics, GAs optimize delivery routes, reducing fuel costs and improving efficiency.
Wind Farm Layout Optimization
- Problem: Maximize energy production while minimizing costs.
- How GAs Solve It:
- Population: Different configurations of turbine placements.
- Fitness Function: Energy output minus installation costs.
- Crossover: Combines high-performing layouts.
