Abstract
Evolutionary Algorithms (EAs) are an effective paradigm for solving many types of optimization problems. They are flexible and can be adapted to new problem classes with little effort. EAs apply operators on the elements of a population. When multiple operators are involved, their distribution is based on fixed probabilities. EAs therefore can not react on changes during an optimization which often leads to premature convergence. In this paper, we present a variation of our approach described in [3] for a self-adapting operator selection that is able to monitor the success of the operators over time and gives priority to currently successful operators. We compare the results with another approach we implemented as first strategy for considering operator success as well as analyze under which circumstances which approach should be preferred.
| Original language | English |
|---|---|
| Title of host publication | IEEE IRI International Conference on Information Reuse and Integration (Heuristic Systems Engineering), Waikoloa, Hawaii, USA, September 16-18, 2006. |
| Number of pages | 5 |
| Publication status | Published - Sept 2006 |
Fields of science
- 102 Computer Sciences
- 102009 Computer simulation
- 102013 Human-computer interaction
- 102019 Machine learning
- 102020 Medical informatics
- 102021 Pervasive computing
- 102022 Software development
- 102025 Distributed systems
- 202017 Embedded systems
- 211902 Assistive technologies
- 211912 Product design
- 102006 Computer supported cooperative work (CSCW)
- 102015 Information systems
- 102016 IT security
- 102027 Web engineering
- 502032 Quality management
- 502050 Business informatics
- 503015 Subject didactics of technical sciences
- 102034 Cyber-physical systems
- 509026 Digitalisation research
- 102040 Quantum computing