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The interactive decision making (IDM) methods exploit the preference information from the decision maker during the optimization task to guide the search towards favourite solution...
Abstract. We present EvA2, a comprehensive metaheuristic optimization framework with emphasis on Evolutionary Algorithms. It presents a modular structure of interfaces and abstract...
MOEA/D is a multi-objective metaheuristic which has shown a remarkable performance when solving hard optimization problems. In this paper, we propose a thread-based parallel versio...
Abstract. Algorithm selection is typically based on models of algorithm performance learned during a separate offline training sequence, which can be prohibitively expensive. In r...
Abstract This paper proposes a flexible software architecture for interactive multiobjective optimization, with a user interface for visualizing the results and facilitating the s...
We demonstrate a means of knowledge discovery through feature extraction that exploits the search history of an optimization run. We regress a symbolic model ensemble from optimiza...
This paper presents a column generation heuristic for the general vehicle routing problem (GVRP), a combined load acceptance and rich vehicle routing problem incorporating various ...