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Although most modern cloud-based enterprise systems, or operating systems, do not commonly allow configurable/automatic termination of processes, tasks or actions, it is common practice for systems administrators to manually terminate, or stop, tasks or actions at any level of the system. The paper investigates the potential of automatic adaptive control with action termination as a method for adapting the system to more appropriate conditions\ud in environments with established goals for both system’s\ud performance and economics. A machine-learning driven control\ud mechanism, employing neural networks, is derived and applied\ud within data-intensive systems. Control policies that have been\ud designed following this approach are evaluated under different\ud load patterns and service level requirements. The experimental\ud results demonstrate performance characteristics and benefits as well as implications of termination control when applied to different action types with distinct run-time characteristics. An automatic termination approach may be eminently suitable for systems with harsh execution time Service Level Agreements, or systems running under conditions of hard pressure on power supply or other constraints. The proposed control mechanisms can be combined with other available toolkits to support deployment of autonomous controllers in high-dimensional enterprise information systems.

Children’s acquisition of English past tense has been widely studied as testing ground for theories of language development, mostly because it comprises a set of quasi-regular mappings. English verbs are of two types: regular verbs, which form their past tense based on a productive rule, and irregular verbs, which form their past tenses through exceptions to that rule. Although many connectionist models exist for capturing language development, few consider individual differences. In this paper, we explore the use of populations of Artificial Neural Networks (ANNs) that evolve according to Behavioural Genetics principles in order to create computational models capable of capturing the population variability exhibited by children in acquiring English past tense verbs. Literature in the field of Behavioural Genetics views variability in children’s learning in terms of genetic and environmental influences. In our model, the effects of genetic influences are simulated through variations in parameters controlling computational properties of ANNs, and the effects of environmental influences are simulated via a filter applied to the training set. This filter alters the quality of information available to the artificial learning system and creates a unique subsample of the training set for each simulated individual. Our approach uses a population of twins to disentangle genetic and environmental influences on past tense performance and to capture the wide range of variability exhibited by children as they learn English past tenses. We use a novel technique to create the population of ANN-twins based on the biological processes of meiosis and fertilisation. This approach allows modelling of both individual differences and development (within the lifespan of an individual) in a single framework. Finally, our approach permits the application of selection on developmental performance on the quasi-regular task across generations. Setting individual differences within an evolutionary framework is an important and novel contribution of our work. We present an experimental evaluation of this model focusing on individual differences in performance. The experiments led to several novel findings including divergence of population attributes during selection to favour regular verbs, irregular verbs, or both; evidence of canalisation, analogous to Waddington’s developmental epigenetic landscape, once selection starts targeting a particular aspect of task domain; and limiting effect on the power of selection in the face of stochastic selection (roulette wheel), sexual reproduction, and a variable learning environment for each individual. Most notably, the heritability of traits showed an inverse relationship to optimisation. Selected traits show lower heritability as the genetic variance of the population reduces. The simulations demonstrate the viability of linking concepts such as heritability of individual differences, cognitive development, and selection over generations within a single computational framework.