Capturing the metaphor of evolutionary transitions in biological complexity, the Evolutionary Transition Algorithm (ETA) evolves solutions of increasing structural and functional complexity from the symbiotic interaction of partial ones. From the definition it follows that this algorithm should be very well suited to solve hierarchically decomposable problems. In this chapter, we show that the ETA can indeed solve this kind of problems effectively. We analyze, in depth, its behavior on hierarchical problems of different size and modular complexity. These results are compared to the Symbiogenetic Model and it is shown that the ETA is more robust and efficient to tackle this kind of problems.
|Title of host publication||Natural Intelligence for Scheduling, Planning and Packing Problems|
|Editors||Raymond Chiong, Sandeep Dhakal|
|Publication status||Published - 29 Sep 2009|
|Name||Natural Intelligence for Scheduling, Planning and Packing Problems|
Chiong, Raymond; Dhakal, Sandeep
- genetic algorithm
- hierarchically decomposable problems