Final answer:
The two main steps of most optimization algorithms are Initialization, which sets up an initial solution, and Iteration, which repeatedly applies procedures to improve the solution until a termination criterion is met.
Step-by-step explanation:
The two main steps of most optimization algorithms are Initialization and Iteration. During the initialization phase, the algorithm starts with an initial solution, which might be randomly generated or based on a heuristic. Then comes the iteration step, where the algorithm applies procedures such as evaluation, selection, and modification to the current solution to find a new, hopefully better, solution. These steps are repeated until a termination condition is met, like reaching a set number of iterations, achieving a solution that is good enough, or no further improvement is possible.
In the context of evolutionary algorithms, terms such as Selection, Crossover, Mutation, and Fitness are used to describe specific processes within the larger optimization procedure. Selection is the process of choosing the best solutions to reproduce, Crossover is a method of recombining those solutions to create new offspring, and Mutation introduces random changes to increase genetic diversity. Fitness, in this regard, is a measurement of how good a particular solution is, relative to others in the population.