Research project "Bridging the Gap between Theory and Practice in Nature-inspired Algorithms"

Funding agency and no.: Danish Council for Independent Research (DFF), 4002-00542


Nature-inspired algorithms such as evolutionary algorithms and ant colony optimization form a class of "off-the-shelf" heuristics for optimization applied in practice when no problem-specific algorithm is available. Despite huge empirical knowledge about nature-inspired algorithms, there is only little theoretically guided advice on the design and application of such algorithms. In particular, existing theory gives little advice on the choice of the abundant parameters that nature-inspired algorithms come with. The aim of this research project is to bridge the gap between theory in practice in nature-inspired algorithms by developing a practically applicable theory of complexity and parameter choice. Results are envisaged which analyze the efficiency on classes of problems for realistic problem sizes and determine the influence of common parameters such as search operators and population size. Such results are expected to improve the design of nature-inspired algorithms and their applicability when optimization problems have to be solved in our society.