J.F. GonÃ§alves
and M.G.C.
Resende

To appear in Handbook of Heuristics, Springer, New York, 2017.

**ABSTRACT**

A
random-key genetic algorithm is an evolutionary metaheuristic for discrete
and global optimization. Each solution is encoded as a vector of N
random keys, where a random key is a real number, randomly generated, in
the continuous interval [0,1). A decoder maps each vector of random
keys to a solution of the optimization problem being solved and computes
its cost. The algorithm starts with a population of P vectors of
random keys. At each iteration, the vectors are partitioned into two
sets, a smaller set of high-valued elite solutions, and the remaining
non-elite solutions. All elite elements are copied, without change,
to the next population. A small number of random-key vectors (the
mutants) is added to the population of the next iteration. The
remaining elements of the population of the next iteration are generated
by combining, with the parametrized uniform crossover, pairs of
solutions. This chapter reviews random-key genetic algorithms
and describes an effective variant called biased random-key genetic
algorithms.

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