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Biased Random-Key Genetic Algorithms: A Review

Mariana A. Londe, Luciana S. Pessoa, Carlos E. Andrade, Mauricio G. C. Resende

TL;DR

Scheduling is by far the most prevalent application area in this review, followed by network design and location problems and the most frequent hybridization method employed is local search.

Abstract

This paper is a comprehensive literature review of Biased Random-Key Genetic Algorithms (BRKGA). BRKGA is a metaheuristic that employs random-key-based chromosomes with biased, uniform, and elitist mating strategies in a genetic algorithm framework. The review encompasses over 150 papers with a wide range of applications, including classical combinatorial optimization problems, real-world industrial use cases, and non-orthodox applications such as neural network hyperparameter tuning in machine learning. Scheduling is by far the most prevalent application area in this review, followed by network design and location problems. The most frequent hybridization method employed is local search, and new features aim to increase population diversity. Overall, this survey provides a comprehensive overview of the BRKGA metaheuristic and its applications and highlights important areas for future research.

Biased Random-Key Genetic Algorithms: A Review

TL;DR

Scheduling is by far the most prevalent application area in this review, followed by network design and location problems and the most frequent hybridization method employed is local search.

Abstract

This paper is a comprehensive literature review of Biased Random-Key Genetic Algorithms (BRKGA). BRKGA is a metaheuristic that employs random-key-based chromosomes with biased, uniform, and elitist mating strategies in a genetic algorithm framework. The review encompasses over 150 papers with a wide range of applications, including classical combinatorial optimization problems, real-world industrial use cases, and non-orthodox applications such as neural network hyperparameter tuning in machine learning. Scheduling is by far the most prevalent application area in this review, followed by network design and location problems. The most frequent hybridization method employed is local search, and new features aim to increase population diversity. Overall, this survey provides a comprehensive overview of the BRKGA metaheuristic and its applications and highlights important areas for future research.
Paper Structure (31 sections, 6 figures)

This paper contains 31 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Fundamentals of the BRKGA
  3. Genetic algorithms and random-keys
  4. The evolutionary process in the BRKGA
  5. Main problems studied with BRKGA
  6. Scheduling
  7. Network configuration
  8. Location
  9. Cutting and Packing
  10. Vehicle Routing
  11. Traveling salesman problem
  12. Clustering
  13. Graph problems
  14. Parameter optimization
  15. Container loading
  16. ...and 16 more sections

Figures (6)

  • Figure 1: Mating process in BRKGA.
  • Figure 1: Number of papers per year and problem. Problem type "other" refers to problems studied in two or less articles.
  • Figure 2: Evolutionary process between consecutive generations.
  • Figure 2: Number of papers per problem. Problem type "other" refers to problems studied in two or less articles.
  • Figure 3: Number of papers per year and hybrid.
  • ...and 1 more figures