A biased random key genetic algorithm for the weighted independent domination problem

Abstract

This work deals with an N P-hard problem in graphs known as the weighted independent domination problem. We propose a biased random key genetic algorithm for solving this problem. The most important part of the proposed algorithm is a decoder that translates any vector of real-values into valid solutions to the tackled problem. The experimental results, in comparison to a state-of-theart population-based iterated greedy algorithm from the literature, show that our proposed approach has advantages over the state-ofthe-art algorithm in the context of the more dense graphs in which edges have higher weights than vertices.

Improved Memetic Algorithm for Solving the Minimum Weight Vertex Independent Dominating Set

“…At the same time, they put forward two different greedy heuristics, where one does not consider the weight of the edge and the other considers the weight of the edge. A biased random key genetic algorithm is proposed for MWIDS [13]. The decoder in the algorithm can transform any real value vector into an effective solution to the problem.…”