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.
The Network Alignment problem is a hard Combinatorial Optimization problem with a wide range of applications, especially in computational biology. Given two (or more) networks, the goal is to find a mapping between their respective nodes that preserves the topological and functional structure of the networks. In this work we extend a novel ant colony optimization approach for network alignment by adding a recently proposed Negative Learning mechanism. In particular, information for Negative Learning is obtained by solving sub-instances of the tackled problem instances at each iteration by means of an Integer Linear Programming solver. The results show that the proposed algorithm not only outperforms the standard ant colony optimization approach but also current state-of-the-art methods from the literature.
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