Single Parent Genetic Algorithm (SPGA)

When implementing the Single Parent Genetic Algorithm in MATLAB, the following steps provide a systematic approach to code development:

1. Population Initialization: Generate initial population using random number generation functions like rand() or randi(), ensuring adequate diversity and solution space coverage. The population size should be determined based on problem complexity and computational constraints.

2. Selection Operation: Evaluate individuals using a fitness function that quantifies solution quality. Implement selection mechanisms such as roulette wheel selection or tournament selection to choose high-fitness individuals as parent candidates. The selection pressure can be controlled through parameters like selection rate or tournament size.

3. Crossover Operation: Select two parent individuals and perform gene segment exchange using crossover operators like single-point crossover or uniform crossover. The crossover rate parameter determines the probability of applying this operation, typically set between 0.6 and 0.9 for balanced exploration-exploitation trade-off.

4. Mutation Operation: Apply random modifications to newly generated offspring using mutation operators such as bit-flip mutation or Gaussian mutation. Mutation rate should be carefully calibrated (usually 0.01-0.1) to maintain population diversity without disrupting good solutions. This operation helps escape local optima and explore new regions of the solution space.

5. Population Update: Combine newly generated offspring with parent individuals to form the next generation population. Implement elitism strategies to preserve best solutions across generations. The algorithm iterates through these steps until meeting termination criteria such as maximum generations, fitness threshold, or convergence stability.

By implementing these steps with proper parameter tuning and problem-specific adaptations, you can develop effective SPGA MATLAB code applicable to various optimization domains including scheduling, routing, and parameter optimization problems.