Random-key genetic algorithms: Principles and applications

2025-06-02

AI Summary

This work addresses discrete and global optimization problems by proposing a unified genetic algorithm framework based on Random Keys (RK). Solutions are encoded as real-valued vectors within the unit hypercube [0,1)ⁿ, where selection, crossover, and mutation operate in the unconstrained continuous space; a problem-specific decoder then maps these vectors to feasible solutions, effectively decoupling the search space from problem constraints. A novel variant—Biased Random Keys (BRK)—is introduced to enhance convergence accuracy and robustness. The framework incorporates parameterized uniform crossover (Spears & De Jong, 1991) and elitist preservation. Experimental evaluation across diverse combinatorial optimization tasks demonstrates that the approach significantly improves generality, maintainability, and cross-problem adaptability, while maintaining high efficiency and stability.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

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.

Problem

• Optimizing discrete and global problems using evolutionary metaheuristics
• Encoding solutions as random-key vectors for genetic operations
• Enhancing framework productivity with elite and mutant solutions

Innovation

• Encodes solutions as random-key vectors
• Uses elite and mutant selection strategy
• Applies parametrized uniform crossover