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…
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…
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…