There is a large class of interesting problems for which no reasonably fast algorithms have been developed. Many of these problems are optimization problems that arise frequently in applications. Given such a hard optimization problem it is often possible to find an efficient algorithm whose solution is approximately optimal. For some hard optimization problems we can use probabilistic algorithms as well — these algorithms do not guarantee the optimum value, but by randomly choosing sufficiently many ``witnesses'' the probability of error may be made as small as we like.
CITATION STYLE
Michalewicz, Z. (1996). GAs: What Are They? In Genetic Algorithms + Data Structures = Evolution Programs (pp. 13–31). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-03315-9_2
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