Christian Blum

TL;DR: It is shown by a simple model-based search algorithm for the node-weighted k-cardinality tree problem that this strategy doesn't guarantee steadily increasing performance of the algorithm in general, and it is rather possible that for some "problem"-"probabilistic model" combinations the average performance is decreasing and even the average probability of sampling good solutions is decreasing over time.

TL;DR: This work defines a new way to transform instance of the classical longest common subsequence problem and of some of its variants into instances of the maximum clique problem and proposes a technique to reduce the size of the resulting graphs.

TL;DR: In this paper, a probabilistic beam search (PBS) algorithm was proposed for solving the Shortest Common Supersequence Problem (SCSPP) with a hybrid beam search and greedy heuristics.

TL;DR: This work proposes an iterative probabilistic tree search algorithm for tackling the minimum common string partition problem and shows the superiority of this approach in comparison to a standard greedy algorithm and a metaheuristic based on ant colony optimization from the related literature.

TL;DR: This work presents and studies an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization, and compares it to some well-known existing negative learning approaches from the related literature.