Random-Key Genetic Algorithms: Principles and Applications

Mariana A. Londe (), Luciana S. Pessoa (), Carlos E. Andrade (), José Fernando Gonçalves and Mauricio G. C. Resende ()
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Mariana A. Londe: Pontifical Catholic University of Rio de Janeiro
Luciana S. Pessoa: Pontifical Catholic University of Rio de Janeiro
Carlos E. Andrade: AT&T Labs Research
José Fernando Gonçalves: Universidade do Porto, Faculdade de Economia da
Mauricio G. C. Resende: University of Washington, Industrial and Systems Engineering

Chapter 30 in Handbook of Heuristics, 2025, pp 921-939 from Springer

Abstract: 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 ) $$[0, 1)$$ . A decoder maps each vector of random keys to a solution of the optimization problem being solved and computes its cost. The benefit of this approach is that all genetic operators and transformations can be maintained within the unitary hypercube, regardless of the problem being addressed. This enhances the productivity and maintainability of the core framework. 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) are 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 of Spears and DeJong (On the virtues of parameterized uniform crossover. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp 230–236, 1991), pairs of solutions. This chapter reviews random-key genetic algorithms and describes an effective variant called biased random-key genetic algorithms.

Keywords: Genetic algorithm; Random keys; Optimization; Random-key genetic algorithm; Biased random-key genetic algorithm; Metaheuristic; Parameter tuning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-00385-0_30

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