1 citations · 1 across the 3 of their papers we have counts for
9 papers
The hop-like problem nature -- unveiling and modelling new features of real-world problems
Michal W. Przewozniczek, Bartosz Frej, Marcin M. Komarnicki
Benchmarks are essential tools for the optimizer's development. Using them, we can check for what kind of problems a given optimizer is effective or not. Since the objective of the…
Obtaining Partition Crossover masks using Statistical Linkage Learning for solving noised optimization problems with hidden variable dependency structure
M. W. Przewozniczek, B. Frej, M. M. Komarnicki +2
In optimization problems, some variable subsets may have a joint non-linear or non-monotonical influence on the function value. Therefore, knowledge of variable dependencies may be…
Limited Perfect Monotonical Surrogates constructed using low-cost recursive linkage discovery with guaranteed output
M. W. Przewozniczek, F. Chicano, R. Tinós +1
Surrogates provide a cheap solution evaluation and offer significant leverage for optimizing computationally expensive problems. Usually, surrogates only approximate the original f…
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. MaciÄ Å¼ek, 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…