27 citations · 32 across the 11 of their papers we have counts for
11 papers
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. 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…
Iterated Local Search with Linkage Learning
Renato Tinós, Michal W. Przewozniczek, Darrell Whitley +1
In pseudo-Boolean optimization, a variable interaction graph represents variables as vertices, and interactions between pairs of variables as edges. In black-box optimization, the…
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…
Incremental Recursive Ranking Grouping for Large Scale Global Optimization
Marcin Michal Komarnicki, Michal Witold Przewozniczek, Halina Kwasnicka +1
Real-world optimization problems may have a different underlying structure. In black-box optimization, the dependencies between decision variables remain unknown. However, some tec…
Multi-Objective Parameter-less Population Pyramid for Solving Industrial Process Planning Problems
Michal Witold Przewozniczek, Piotr Dziurzanski, Shuai Zhao +1
Evolutionary methods are effective tools for obtaining high-quality results when solving hard practical problems. Linkage learning may increase their effectiveness. One of the stat…