Name
Abstract | ||
|---|---|---|
The importance of domain knowledge in the design of effective evolutionary algorithms (EAs) is widely acknowledged in the meta-heuristics community. In the last few decades, a plethora of EAs has been manually designed by domain experts for solving domain-specific problems. Specialization has been achieved mainly by embedding available domain knowledge into the algorithms. Although programming libraries have been made available to construct EAs, a unifying framework for designing specialized EAs across different problem domains and branches of evolutionary computing does not exist yet. In this paper, we address this issue by introducing an evolutionary computing modeling language (ECML) which is based on the unified modeling language (UML). ECML incorporates basic UML elements and introduces new extensions that are specially needed for the evolutionary computation domain. Subsequently, the concept of meta evolutionary algorithms (MEAs) is introduced as a family of EAs that is capable of interpreting ECML. MEAs are solvers that are not restricted to a particular problem domain or branch of evolutionary computing through the use of ECML. By separating problem-specific domain knowledge from the EA implementation, we show that a unified framework for evolutionary computation can be attained. We demonstrate our approach by applying it to a number of examples. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TEVC.2010.2081368 | IEEE Trans. Evolutionary Computation |
Keywords | Field | DocType |
evolutionary computing modeling language,different problem domain,evolutionary computing,evolutionary computation domain,evolutionary computation,meta evolutionary algorithm,effective evolutionary algorithm,domain knowledge,domain expert,available domain knowledge,context modeling,high level language,uml,unified modeling language,evolutionary algorithm,algorithm design,context model,high level languages,indexing terms,computational modeling,encoding,computer model,evolutionary algorithms,modeling language,algorithm design and analysis,computer and information science | Evolutionary algorithm,Unified Modeling Language,Domain knowledge,Problem domain,Computer science,Evolutionary computation,Modeling language,Theoretical computer science,Artificial intelligence,Evolutionary programming,Evolutionary music,Machine learning | Journal |
Volume | Issue | ISSN |
15 | 2 | 1089-778X |
Citations | PageRank | References |
4 | 0.45 | 18 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aydt, H. | 1 | 4 | 0.45 |
| S. J. Turner | 2 | 79 | 7.57 |
| Wentong Cai | 3 | 1928 | 197.81 |
| M. Y.H. Low | 4 | 31 | 2.55 |
| Yew-Soon Ong | 5 | 4205 | 224.11 |
| Rassul Ayani | 6 | 32 | 5.81 |