Learning and reusing information in space layout problems using genetic engineering

The paper describes the application of a genetic engineering based extension to genetic algorithms to the layout planning problem. We study the gene evolution which takes place when an algorithm of this type is running and demonstrate that in many cases it effectively leads to the partial decomposition of the layout problem by grouping some activit ies together and optimally placing these groups during the first stage of the computation. At a second stage it optimally places activities within these groups. We show that the algorithm finnds the solution faster than standard evolutionary methods and that evolved genes represent design features that can be re-used later in a range of similar problems.