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Fitness landscape analysis (FLA) is a useful tool in the domain of (meta-)heuristic optimization but depends on explicitly knowing what fitness value is assigned to each solution. Dynamic optimization problems often do not provide their fitness landscape in such an explicit form, but by employing problem-specific knowledge, information about the problem itself and its current state can still be obtained. In this paper, a type of gray-box analysis of states of the open-ended stacking problem in two variations is presented. The current states obtained by monitoring the problem and algorithm during optimization are described via statistical measures similar to FLA measures. From this the distribution of possible states (the state landscape) and the transitions between problem states are analyzed. Visualization of the empirically obtained results reveals insights into algorithm-problem dynamics.
|Title of host publication||GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||8|
|Publication status||Published - 7 Jul 2021|
|Event||2021 Genetic and Evolutionary Computation Conference, GECCO 2021 - Virtual, Online, France|
Duration: 10 Jul 2021 → 14 Jul 2021
|Name||GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion|
|Conference||2021 Genetic and Evolutionary Computation Conference, GECCO 2021|
|Period||10.07.2021 → 14.07.2021|
- dynamic optimization
- fitness landscape analysis
- state space analysis
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