Genetic Algorithms

Genetic algorithms, developed by John Holland in the 1960s and refined over time, are optimization techniques inspired by Darwin’s principle of natural selection. This principle posits that all species share a common ancestor and evolve over time, with natural selection favoring those best adapted to their environment.

The elegance of genetic algorithms lies in their simplicity. Instead of exhaustively analyzing countless possibilities, they generate potential solutions, evaluate their performance using a scoring system, and evolve them towards an optimal solution. Poor-performing candidates are discarded, while the best ones are retained and slightly modified to create new variants. These new variants are then evaluated, and the process repeats until a satisfactory solution is found.

Holland’s formal description of genetic algorithms uses terms like crossover, fitness, and mutation to draw parallels with natural selection. For this analogy to be effective, it’s crucial to have a well-defined objective function to evaluate solutions, an adequate population size, an appropriate mutation rate, and an effective crossover procedure to generate new solutions from existing ones.

A genetic algorithm typically follows these steps:

1. Select an initial population: Each member represents a potential solution to the problem.
2. Evaluate each individual: Use the chosen objective function to assign a “fitness score” to each member.
3. Eliminate low-scoring individuals: Remove those with the lowest fitness scores.
4. Generate new individuals: Create new members by mutating or combining the top performers.
5. Incorporate new individuals: Add these new members to the population.

Repeat steps 2 through 5 until a set amount of time has passed, a specific number of generations have been tested, or no further improvements in fitness are observed. The individual with the highest fitness score at the end of the process is considered the solution to the problem.