27 citations · 32 across the 11 of their papers we have counts for
5 papers · 1 filter
Subfunction Structure Matters: A New Perspective on Local Optima Networks
S. L. Thomson, M. W. Przewozniczek
Local optima networks (LONs) capture fitness landscape information. They are typically constructed in a black-box manner; information about the problem structure is not utilised. T…
On Revealing the Hidden Problem Structure in Real-World and Theoretical Problems Using Walsh Coefficient Influence
M. W. Przewozniczek, F. Chicano, R. Tinós +3
Gray-box optimization employs Walsh decomposition to obtain non-linear variable dependencies and utilize them to propose masks of variables that have a joint non-linear influence o…
Seeking and leveraging alternative variable dependency concepts in gray-box-elusive bimodal land-use allocation problems
J. Maciążek, M. W. Przewozniczek, J. Schwaab
Solving land-use allocation problems can help us to deal with some of the most urgent global environmental issues. Since these problems are NP-hard, effective optimizers are needed…
Moving between high-quality optima using multi-satisfiability characteristics in hard-to-solve Max3Sat instances
J. Piatek, M. W. Przewozniczek, F. Chicano +1
Gray-box optimization proposes effective and efficient optimizers of general use. To this end, it leverages information about variable dependencies and the subfunction-based proble…
Availability of Perfect Decomposition in Statistical Linkage Learning for Unitation-based Function Concatenations
Michal Prusik, Bartosz Frej, Michal W. Przewozniczek
Statistical Linkage Learning (SLL) is a part of many state-of-the-art optimizers. The purpose of SLL is to discover variable interdependencies. It has been shown that the effective…