Making the Edge-Set Encoding Fly by Controlling the Bias of its Crossover Operator

The edge-set encoding is a direct tree encoding which applies search operators directly to trees represented as sets of edges. There are two variants of crossover operators for the edge-set encoding: With heuristics that consider the weights of the edges, or without heuristics. Due to a strong bias of the heuristic crossover operator towards the minimum spanning tree (MST) a population of solutions converges quickly towards the MST and EAs using this operator show low performance when used for tree optimization problems where the optimal solution is not the MST

On the Bias and Performance of the Edge-Set Encoding

The edge-set encoding is a direct encoding for trees which directly represents trees as sets of edges. In contrast to indirect representations, where usually standard operators are applied to a list of strings and the resulting phenotype is constructed by an appropriate genotype-phenotype mapping, encoding-specific initialization, crossover, and mutation operators have been developed for the edge-set encoding, which are directly applied to trees. There are two different variants of operators: heuristic versions that consider the weights of the edges and non-heuristic versions. An investigation into the bias of the different variants of the operators shows that the heuristic variants are biased towards the minimum spanning tree (MST), that means solutions similar to the MST are favored. In contrast, non-heuristic versions are unbiased. The performance of edgesets is investigated for the optimal communication spanning tree (OCST) problem. Results are presented for randomly created problems as well as for test instances from the literature. Although optimal solutions for the OCST problem are similar to the MST, evolutionary algorithms using the heuristic crossover operator fail if the optimal solution is only slightly different from the MST. The non-heuristic version shows similar performance as the network random key encoding, which is an unbiased indirect encoding and is used as a benchmark. With proper parameter setting the heuristic version of the mutation operator shows good results for the OCST problem as it can make use of the fact that optimal solutions of the OCST problem are similar to the MST. The results suggest that the heuristic crossover operator of the edge-set encoding should not be used for tree problems as its bias towards the MST is too strong

Risk Factors for Early Dialysis Dependency in Autosomal Recessive Polycystic Kidney Disease

© 2018 Elsevier Inc.Objective: To identify prenatal, perinatal, and postnatal risk factors for dialysis within the first year of life in children with autosomal recessive polycystic kidney disease (ARPKD) as a basis for parental counseling after prenatal and perinatal diagnosis. Study design: A dataset comprising 385 patients from the ARegPKD international registry study was analyzed for potential risk markers for dialysis during the first year of life. Results: Thirty-six out of 385 children (9.4%) commenced dialysis in the first year of life. According to multivariable Cox regression analysis, the presence of oligohydramnios or anhydramnios, prenatal kidney enlargement, a low Apgar score, and the need for postnatal breathing support were independently associated with an increased hazard ratio for requiring dialysis within the first year of life. The increased risk associated with Apgar score and perinatal assisted breathing was time-dependent and vanished after 5 and 8 months of life, respectively. The predicted probabilities for early dialysis varied from 1.5% (95% CI, 0.5%-4.1%) for patients with ARPKD with no prenatal sonographic abnormalities to 32.3% (95% CI, 22.2%-44.5%) in cases of documented oligohydramnios or anhydramnios, renal cysts, and enlarged kidneys. Conclusions: This study, which identified risk factors associated with onset of dialysis in ARPKD in the first year of life, may be helpful in prenatal parental counseling in cases of suspected ARPKD