Definition
Genetic Algorithms (GA) are search and optimization techniques inspired by the process of natural selection. They maintain a population of candidate solutions and evolve them over generations using operations such as mutation, recombination (crossover), and selection based on a “fitness function.”
Why It Matters
Genetic algorithms allow us to harness the power of evolution to solve problems that are too complex for human intuition; they often ‘discover’ radical, organic designs that outperform traditional engineering, providing a path to optimization where no clear ‘manual’ solution exists.
Core Concepts
- Population-Based Search: Instead of improving a single solution, GAs maintain a pool of candidates, allowing for exploration of multiple areas of the search space simultaneously.
- Genetic Operators:
- Mutation: Small, random changes to a candidate solution.
- Recombination (Crossover): Combining parts of two “parent” solutions to create an “offspring.”
- Selection: The “survival of the fittest,” where better solutions are more likely to be chosen for the next generation.
- Fitness Function: The objective function that evaluates how well a candidate solution solves the target problem.
- Representational Format: The way a solution is encoded (e.g., bit strings, trees, programs). The success of a GA often depends on finding a “genetic language” that matches the structure of the problem.
- Stochastic Hill-Climbing: GAs can be viewed as a form of stochastic search that gradually climbs the “fitness landscape” toward an optimum.