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… (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… (More)
We consider the problem of maintaining a minimum spanning tree of a dynamically changing graph, subject to changes on edge weights. We propose an on-line fully-dynamic algorithm that runs in time O(|E|) when the easy-to-implement DRD-trees data structure for dynamic trees is used. Numerical experiments illustrate the efficiency of the approach.
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… (More)
In this paper we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic… (More)