A Memetic Approach to the Nurse Rostering Problem
Applied Intelligence
Automated discovery of local search heuristics for satisfiability testing
Evolutionary Computation
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Hyper-heuristics with low level parameter adaptation
Evolutionary Computation
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We present an object oriented framework for designing and evaluating heuristic search algorithms that achieve a high level of generality and work well on a wide range of combinatorial optimization problems. Our framework, named HyFlex, differs from most software tools for meta-heuristics and evolutionary computation in that it provides the algorithm components that are problem-specific instead of those which are problem-independent. In this way, we simultaneously liberate algorithm designers from needing to know the details of the problem domains; and prevent them from incorporating additional problem specific information in their algorithms. The efforts need instead to be focused on designing high-level strategies to intelligently combine the provided problem specific algorithmic components. We plan to propose a challenge, based on our framework, where the winners will be those algorithms with a better overall performance across all of the different domains. Using an Olympic metaphor, we are not solely focussed on the 100 meters race, but instead on the decathlon of modern search methodologies.