Abstract. Citation Chaves et al., A parallel branch-and-cut and an adaptive metaheuristic to solve the Family Traveling Salesman Problem. Submitted to …
We study the runtime performance of three types of random-key genetic algorithms: the unbiased algorithm of Bean (1994); the biased algorithm of Gonçalves and Resende (2011); and a greedy version of Bean’s algorithm on 12 instances from four types of covering problems: general-cost set covering, Steiner triple covering, general-cost set K-covering, and unit-cost covering by … Read more
We present a novel multi-population biased random-key genetic algorithm (BRKGA) for the 2D and 3D bin packing problem. The approach uses a maximal-space representation to manage the free spaces in the bins. The proposed algorithm uses a decoder based on a novel placement procedure within a multi-population genetic algorithm based on random keys. The BRKGA … Read more
In this paper, we describe brkgaAPI, an efficient and easy-to-use object oriented application programming interface for the algorithmic framework of biased random-key genetic algorithms. Our cross-platform library automatically handles the large portion of problem-independent modules that are part of the framework, including population management and evolutionary dynamics, leaving to the user the task of implementing … Read more
This paper surveys several applications of biased random-key genetic algorithms (BRKGA) in optimization problems that arise in telecommunications. We first review the basic concepts of BRKGA. This is followed by a description of BRKGA-based heuristics for routing in IP networks, design of survivable IP networks, redundant server location for content distribution, regenerator location in optical … Read more
We present a biased random-key genetic algorithm (BRKGA) for finding small covers of computationally difficult set covering problems that arise in computing the 1-width of incidence matrices of Steiner triple systems. Using a parallel implementation of the BRKGA, we compute improved covers for the two largest instances in a standard set of test problems used … Read more
Random-key genetic algorithms were introduced by Bean (1994) for solving sequencing problems in combinatorial optimization. Since then, they have been extended to handle a wide class of combinatorial optimization problems. This paper presents a tutorial on the implementation and use of biased random-key genetic algorithms for solving combinatorial optimization problems. Biased random-key genetic algorithms are … Read more