Phenotype building blocks and geometric crossover in structural optimisation

Macro or phenotype building blocks (PBBs) contain information at phenotype level. PBBs are either the components of a multi-component system, or different parts of a continuous system with different design qualities and/or evaluation measures. Using PBBs can lead to enhancement of the search efficiency by utilising problem specific search operators and heuristics applied on the PBBs. In order to preserve, propagate and recombine good building blocks efficiently, building blocks should have a low probability of being disrupted by crossover. Therefore, the crossover operation should be designed at phenotype level. Geometric crossover (GCO) is applied on the phenotype rather than the genotype. Identifying PBBs and using GCO, partial fitness can be defined and employed to improve the performance of the search algorithm. Another advantage of using GCO is as a result of its ease of application to nonfixed- length and variable-length chromosomes. Variable-length chromosomes are a common feature of topology optimisation problems when genetic algorithms are employed. GCO can be easily applied to the structural optimisation problems including, topology optimisation without predefining a grid. These two advantages have been demonstrated by implementing GCO in genetic algorithms employed for optimisation of plane trusses supporting distributed loads, two-dimensional steel frames and wind turbine blades.