Random-key genetic algorithms: Principles and applications

TL;DR

This paper reviews random-key genetic algorithms, a flexible evolutionary optimization method that encodes solutions as real-valued vectors within the unit interval, emphasizing their principles, variants, and applications.

Contribution

It provides a comprehensive overview of random-key genetic algorithms, introduces an effective biased variant, and discusses their advantages for discrete and global optimization.

Findings

Random-key GAs encode solutions as vectors in [0,1) for flexibility.

Biased random-key GAs improve convergence and solution quality.

The approach enhances framework maintainability across problems.

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 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) 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 of Spears and DeJong (1991), pairs of solutions. This chapter reviews random-key genetic algorithms and describes an effective variant called biased random-key genetic algorithms.