Evolutionary dynamic optimisation of airport security lane schedules

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Reducing costs whilst maintaining passenger satisfaction is an important problem for airports. One area this can be applied is the security lane checks at the airport. However, reducing costs through reducing lane openings typically increases queue length and hence passenger dissatisfaction. This paper demonstrates that evolutionary methods can be used to optimise airport security lane schedules such that passenger dissatisfaction and staffing costs can be minimised. However, it is shown that these schedules typically over-fit the forecasts of passenger arrivals at security such that in actuality significant passenger delays can occur with deviations from the forecast. Consequently, this paper further demonstrates that dynamic evolutionary re-optimisation of these schedules can significantly mitigate this over-fitting problem with much reduced passenger delays

An Evolutionary Approach to Active Robust Multiobjective Optimisation

An Active Robust Optimisation Problem (AROP) aims at finding robust adaptable solutions, i.e. solutions that actively gain robustness to environmental changes through adaptation. Existing AROP studies have considered only a single performance objective. This study extends the Active Robust Optimisation methodology to deal with problems with more than one objective. Once multiple objectives are considered, the optimal performance for every uncertain parameter setting is a set of configurations, offering different trade-offs between the objectives. To evaluate and compare solutions to this type of problems, we suggest a robustness indicator that uses a scalarising function combining the main aims of multi-objective optimisation: proximity, diversity and pertinence. The Active Robust Multi-objective Optimisation Problem is formulated in this study, and an evolutionary algorithm that uses the hypervolume measure as a scalarasing function is suggested in order to solve it. Proof-of-concept results are demonstrated using a simplified gearbox optimisation problem for an uncertain load demand

Genome variations: Effects on the robustness of neuroevolved control for swarm robotics systems

Manual design of self-organized behavioral control for swarms of robots is a complex task. Neuroevolution has proved a viable alternative given its capacity to automatically synthesize controllers. In this paper, we introduce the concept of Genome Variations (GV) in the neuroevolution of behavioral control for robotic swarms. In an evolutionary setup with GV, a slight mutation is applied to the evolving neural network parameters before they are copied to the robots in a swarm. The genome variation is individual to each robot, thereby generating a slightly heterogeneous swarm. GV represents a novel approach to the evolution of robust behaviors, expected to generate more stable and robust individual controllers, and bene t swarm behaviors that can deal with small heterogeneities in the behavior of other members in the swarm. We conduct experiments using an aggregation task, and compare the evolved solutions to solutions evolved under ideal, noise-free conditions, and to solutions evolved with traditional sensor noise.info:eu-repo/semantics/acceptedVersio

Influence of learning on range expansion and adaptation to novel habitats

Learning has been postulated to ‘drive’ evolution, but its influence on adaptive evolution in heterogeneous environments has not been formally examined. We used a spatially explicit individual-based model to study the effect of learning on the expansion and adaptation of a species to a novel habitat. Fitness was mediated by a behavioural trait (resource preference), which in turn was determined by both the genotype and learning. Our findings indicate that learning substantially increases the range of parameters under which the species expands and adapts to the novel habitat, particularly if the two habitats are separated by a sharp ecotone (rather than a gradient). However, for a broad range of parameters, learning reduces the degree of genetically-based local adaptation following the expansion and facilitates maintenance of genetic variation within local populations. Thus, in heterogeneous environments learning may facilitate evolutionary range expansions and maintenance of the potential of local populations to respond to subsequent environmental changes

Robustness and evolutionary dynamic optimisation of airport security schedules

Reducing security lane operations whilst minimising passenger waiting times in unforseen circumstances is important for airports. Evolutionary methods can design optimised schedules but these tend to over-fit passenger arrival forecasts resulting in lengthy waiting times for unforeseen events. Dynamic re-optimisation can mitigate for this issue but security lane schedules are an example of a constrained problem due to the human element preventing major modifications. This paper postulates that for dynamic re-optimisation to be more effective in constrained circumstances consideration of schedule robustness is required. To reduce over-fitting a simple methodology for evolving more robust schedules is investigated. Random delays are introduced into forecasts of passenger arrivals to better reflect actuality and a range of these randomly perturbed forecasts are used to evaluate schedules. These steps reduced passenger waiting times for actual events for both static and dynamic policies with minimal increases in security operations

The implications of nongenetic inheritance for evolution in changing environments

Nongenetic inheritance is a potentially important but poorly understood factor in population responses to rapid environmental change. Accumulating evidence indicates that nongenetic inheritance influences a diverse array of traits in all organisms and can allow for the transmission of environmentally induced phenotypic changes (‘acquired traits’), as well as spontaneously arising and highly mutable variants. We review models of adaptation to changing environments under the assumption of a broadened model of inheritance that incorporates nongenetic mechanisms of transmission, and survey relevant empirical examples. Theory suggests that nongenetic inheritance can increase the rate of both phenotypic and genetic change and, in some cases, alter the direction of change. Empirical evidence shows that a diversity of phenotypes – spanning a continuum from adaptive to pathological – can be transmitted nongenetically. The presence of nongenetic inheritance therefore complicates our understanding of evolutionary responses to environmental change. We outline a research program encompassing experimental studies that test for transgenerational effects of a range of environmental factors, followed by theoretical and empirical studies on the population-level consequences of such effects

From evolutionary computation to the evolution of things

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems

Label- and amplification-free electrochemical detection of bacterial ribosomal RNA

Current approaches to molecular diagnostics rely heavily on PCR amplification and optical detection methods which have restrictions when applied to point of care (POC) applications. Herein we describe the development of a label-free and amplification-free method of pathogen detection applied to Escherichia coli which overcomes the bottleneck of complex sample preparation and has the potential to be implemented as a rapid, cost effective test suitable for point of care use. Ribosomal RNA is naturally amplified in bacterial cells, which makes it a promising target for sensitive detection without the necessity for prior in vitro amplification. Using fluorescent microarray methods with rRNA targets from a range of pathogens, an optimal probe was selected from a pool of probe candidates identified in silico. The specificity of probes was investigated on DNA microarray using fluorescently labeled 16S rRNA target. The probe yielding highest specificity performance was evaluated in terms of sensitivity and a LOD of 20 pM was achieved on fluorescent glass microarray. This probe was transferred to an EIS end point format and specificity which correlated to microarray data was demonstrated. Excellent sensitivity was facilitated by the use of uncharged PNA probes and large 16S rRNA target and investigations resulted in an LOD of 50 pM. An alternative kinetic EIS assay format was demonstrated with which rRNA could be detected in a species specific manner within 10-40 min at room temperature without wash steps