Diabetic kidney disease (DKD) represents a major component of the health burden associated with type 1 and type 2 diabetes. Recent advances have produced an explosion of ‘novel’ assay‐based risk markers for DKD, though clinical use remains restricted. Although many patients with progressive DKD follow a classical albuminuria‐based pathway, non‐albuminuric DKD progression is now well recognized. In general, the following clinical and biochemical characteristics have been associated with progressive DKD in both type 1 and type 2 diabetes: increased hemoglobin A1c, systolic blood pressure, albuminuria grade, early glomerular filtration rate decline, duration of diabetes, age (including pubertal onset) and serum uric acid; the presence of concomitant microvascular complications; and positive family history. The same is true in type 2 diabetes for male sex category, in patients following an albuminuric pathway to DKD, and also true for the presence of increased pulse wave velocity. The following baseline clinical characteristics have been proposed as risk factors for DKD progression, but with further research required to assess the nature of any relationship: dyslipidemia (including low‐density lipoprotein, total and high‐density lipoprotein cholesterol); elevated body mass index; smoking status; hyperfiltration; decreases in vitamin D, hemoglobin and uric acid excretion (all known consequences of advanced DKD); and patient test result visit‐to‐visit variability (hemoglobin A1c, blood pressure and high‐density lipoprotein cholesterol). The development of multifactorial ‘renal risk equations’ for type 2 diabetes has the potential to simplify the task of DKD prognostication; however, there are currently none for type 1 diabetes‐specific populations. Significant progress has been made in the prediction of DKD progression using readily available clinical data, though further work is required to elicit the role of several variables, and to consolidate data to facilitate clinical implementation.

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Abstract.A formal, representation-independent form of a memetic algorithm--a genetic algorithm incorporating local search--is introduced. A generalised form of N-point crossover is defined together with representation-independent patching and hill-climbing operators. The resulting formal algorithm is then constructed and tested empirically on the ~ravelling sales-rep problem. Whereas the genetic algorithms tested were unable to make good progress on the problems studied, the memefic algorithms performed very well. MotivationThe rSle of local search in the context of genetic algorithms and the wider field of evolutionary computing has been much discussed. The traditional view, which can be traced back to Holland (1975), has been that the primary search operator in evolutionary computing should be recombination. In its most extreme form, this View casts mutation and other local operators as mere adjuncts to recombination, playing auxiliary (if important) rSles such as keeping the gene pool well stocked and helping to tune final solutions. There have, however, long been advocates of a greater rSle for mutation, hill-climbing and local refinement. The arguments for serious consideration of operators other than recombination for primary search come in many forms and are inspired by widely differing applications. For example, Davis (1991) advocates hybridisation of genetic algorithms with domain-specific techniques for "real world" optimisation, by incorporating extra move operators. He regularly uses sophisticated decoders that make use, for example, of greedy algorithms and repair mechanisms. Ackley (1987) recommends genetic hillclimbing, in which crossover plays a rather less dominant rSle. Muehlenbein (1992) argues theoretically and Gorges-Schleuter (1989) provides empirical demonstrations that local search can play a key r01e, and Muehlenbein (1989) incorporates it as a fundamental component of his particular notion Of a parallel genetic algorithm with a structured population. Meanwhile, the Evolution Strategies community has always placed more emphasis on mutation than crossover (Baeck et al., 1991). Countless other advocates of a greater emphasis on non-recombinative elements of evolutionary search could be cited, especially from the ranks of those competing with domain-specific techniques.Moscato & Norman (1992) have introduced the term memetic algorithm to describe evolutionary algorithms in which local search plays a significant part. This term is motivated by Richard Dawkins's notion of a meme as a unit of information that reproduces itself as people exchange ideas (Dawkins, 1976), A key difference exists between genes

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