The simplest form of genetic algorithm involves 3 types of operator: Selection, Cross over i.e. single point algorith and Mutation.

Crossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next. It is analogous to reproduction and biological crossover, upon which genetic algorithms are based. Crossover is a process of taking more than one parent solution and producing a child solution from them. There are methods for selection of the chromosomes.

SPA investigate only one marker independent of other loci whereas SA generate and uses random loci :)

In simple words: single point algorithm operates on single point in search space and this point is gradually evolved, in a well defined way, hopefully to get closer to better location. A Newton method or its Levenberg-Marquardt version is a good example of such behavior. Similarly, in Monte-Carlo-type procedures we operate with a single trial solution at a time. In GA, PSO and other 'nature inspired' algorithms the situation is different. Each next step depends on collective behavior of many trial points at once: for example they attract or repel each other. Additionally, the population size may be or may be not fixed. In this sense the good old simplex algorithm might be classified as belonging to 'many points' group.