J.F. Gonçalves 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.

PDF file of full paper

Go back

Mauricio G.C. Resende's Home Page

Copyright Notice