Page 94 - Data Science Algorithms in a Week

78 Fred K. Gruber

 selection is according to a fitness function only, and
 they use probabilistic transition rules.

The search for a solution implies a compromise between two contradictory
requirements: exploitation of the best available solution, and robust exploration of the
search space. Exploitation is referred to the search of similar solutions and it is closely
related to the crossover operator while exploration involves a global search and it is
related to the mutation operator. If the solutions are overexploited, a premature
convergence of the search procedure may occur. This means that the search stops
progressing and the procedure eventually ends with a suboptimal solution. If emphasis is
given to the exploration, the information already available may be lost and the
convergence of the search process could become very slow.

Figure 1. Simple Genetic Algorithm.

Probably, the most important characteristics of genetic algorithms are the
robustness—they tend to solve a wide domain of problems with relatively efficiency—
and the flexibility—they do not require any especial information about the problem (e.g.,
derivatives, etc.) besides the fitness function. Thanks to these characteristics, they have
been applied to a great variety of problems