Learning inexpensive parametric design models using an augmented genetic programming technique

Previous applications of Genetic Programming (GP) have been restricted to searching for algebraic approximations mapping the design parameters (e.g. geometrical parameters) to a single design objective (e.g. weight). In addition, these algebraic expressions tend to be highly complex. By adding a simple extension to the GP technique, a powerful design data analysis tool is developed. This paper significantly extends the analysis capabilities of GP by searching for multiple simple models within a single population by splitting the population into multiple islands according to the design variables used by individual members. Where members from different islands `cooperate', simple design models can be extracted from this cooperation. This relatively simple extension to GP is shown to have powerful implications to extracting design models that can be readily interpreted and exploited by human designers. The full analysis method, GP-HEM (Genetic Programming Heuristics Extraction Method), is described and illustrated by means of a design case study

Abnormal brain scan with subacute extradural haematomas

Four patients are described with proven subacute extradural haematomas, each with an abnormal cerebral scan of diagnostic assistance. A possible mechanism of production of the subacute extradural haematoma is discussed, and appears to be similar to the mechanism involved in the subacute subdural haematoma. The means by which the abnormal scan results in such cases is also examined, from which it appears that non-specific meningeal membrane inflammatory reaction surrounding the haematoma is significant