What is a genetic algorithm?
A search and optimisation method inspired by natural selection, evolving solutions over generations through selection, crossover, and mutation
Truncation selection
evaluating the fitness of the whole population… All of the parents come from top scoring X%
Tournament Selection
Where a small random subset of individuals is chosen from the population, and the fittest among them is selected for reproduction.
Elitism
Elitism preserves top solutions across generations, improving convergence speed but potentially reducing genetic diversity.
Genotype Mapping
Encoded data structure representing a candidate solution, manipulated by genetic operators during search.
Phenotype Mapping
Genotype decoded, executable solution form evaluated by the fitness function within the problem domain.
G —> P Mapping
Process of decoding encoded solutions into evaluable forms for fitness assessment in problem space.
Steady State (GA)
Instead of generating an entirely new population at each iteration, the SSGA selects a small number of individuals (usually one or two) for replacement in each generation.
Uniform Crossover
Each bit is chosen from either parent with equal probability
