LCS & GBML Central

Abstract  This study focuses on the use of genetic programming to automate the design of robust analog circuits. We define two complementary
types of failure modes: partial short-circuit and partial disconnect, and demonstrated novel circuits that are resilient across
a spectrum of fault levels. In particular, we focus on designs that are uniformly robust, and unlike designs based on redundancy,
do not have any single point of failure. We also explore the complementary problem of designing tamper-proof circuits that
are highly sensitive to any change or variation in their operating conditions. We find that the number of components remains
similar both for robust and standard circuits, suggesting that the robustness does not necessarily come at significant increased
circuit complexity. A number of fitness criteria, including surrogate models and co-evolution were used to accelerate the
evolutionary process. A variety of circuit types were tested, and the practicality of the generated solutions was verified
by physically constructing the circuits and testing their physical robustness.

Abstract  Using multiobjective genetic programming with a complexity objective to overcome tree bloat is usually very successful but
can sometimes lead to undesirable collapse of the population to all single-node trees. In this paper we report a detailed
examination of why and when collapse occurs. We have used different types of crossover and mutation operators (depth-fair
and sub-tree), different evolutionary approaches (generational and steady-state), and different datasets (6-parity Boolean
and a range of benchmark machine learning problems) to strengthen our conclusion. We conclude that mutation has a vital role
in preventing population collapse by counterbalancing parsimony pressure and preserving population diversity. Also, mutation
controls the size of the generated individuals which tends to dominate the time needed for fitness evaluation and therefore
the whole evolutionary process. Further, the average size of the individuals in a GP population depends on the evolutionary
approach employed. We also demonstrate that mutation has a wider role than merely culling single-node individuals from the
population; even within a diversity-preserving algorithm such as SPEA2 mutation has a role in preserving diversity.