Phase Transitions and Symmetry Breaking in Genetic Algorithms with Crossover

In this paper, we consider the role of the crossover operator in genetic algorithms. Specifically, we study optimisation problems that exhibit many local optima and consider how crossover affects the rate at which the population breaks the symmetry of the problem. As an example of such a problem, we consider the subset sum problem. In so doing, we demonstrate a previously unobserved phenomenon, whereby the genetic algorithm with crossover exhibits a critical mutation rate, at which its performance sharply diverges from that of the genetic algorithm without crossover. At this critical mutation rate, the genetic algorithm with crossover exhibits a rapid increase in population diversity. We calculate the details of this phenomenon on a simple instance of the subset sum problem and show that it is a classic phase transition between ordered and disordered populations. Finally, we show that this critical mutation rate corresponds to the transition between the genetic algorithm accelerating or preventing symmetry breaking and that the critical mutation rate represents an optimum in terms of the balance of exploration and exploitation within the algorithm

HETEROGENEOUS BASE METAL CATALYZED OXIDATIVE DEPOLYMERIZATION OF LIGNIN AND LIGNIN MODEL COMPOUNDS

With the dwindling availability of petroleum, focus has shifted to renewable energy sources such as lignocellulosic biomass. Lignocellulosic biomass is composed of three main constituents, lignin, cellulose and hemicellulose. Due to the low value of cellulosic ethanol, utilization of the lignin component is necessary for the realization of an economically sustainable biorefinery model. Once depolymerized, lignin has the potential to replace petroleum-derived molecules used as bulk and specialty aromatic chemicals. Numerous lignin depolymerization strategies focus on cleavage of β-aryl ether linkages, usually at high temperatures and under reductive conditions. Alternatively, selective benzylic oxidation strategies have recently been explored for lignin and lignin models. In this work, heterogeneous catalytic methods using supported base metals and layered-double hydroxides were evaluated for the oxidation of lignin models both before and after benzylic oxidation. Additionally, by studying putative reaction intermediates, insights were gained into the mechanisms of oxidative fragmentation of the model compounds. Generally, it was found that after benzylic oxidation models were more susceptible to oxidative fragmentation. Indeed, several heterogeneous oxidation systems were found to convert lignin models to oxygenated aryl monomers (mainly benzoic acids and phenols) using inexpensive primary oxidants (i.e., hydrogen peroxide and molecular oxygen). Reactions were conducted at relatively mild temperatures and at low oxygen concentrations for the purpose of an easy transition to large-scale experiments. Finally, the catalytic systems that resulted in significant cleavage of lignin models were applied to a Kraft lignin. Oxidation of Kraft lignin resulted a mixture of products for which analytical data and increased solubility are consistent with interunit cleavage within the lignin macromolecule

Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation

The real-world testing of decisions made using causal machine learning models is an essential prerequisite for their successful application. We focus on evaluating and improving contextual treatment assignment decisions: these are personalised treatments applied to e.g. customers, each with their own contextual information, with the aim of maximising a reward. In this paper we introduce a model-agnostic framework for gathering data to evaluate and improve contextual decision making through Bayesian Experimental Design. Specifically, our method is used for the data-efficient evaluation of the regret of past treatment assignments. Unlike approaches such as A/B testing, our method avoids assigning treatments that are known to be highly sub-optimal, whilst engaging in some exploration to gather pertinent information. We achieve this by introducing an information-based design objective, which we optimise end-to-end. Our method applies to discrete and continuous treatments. Comparing our information-theoretic approach to baselines in several simulation studies demonstrates the superior performance of our proposed approach.Comment: ICML 2022 Workshop on Adaptive Experimental Design and Active Learning in the Real World. 16 pages, 5 figure

Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpainting

Medical segmentation is performed to determine the bounds of regions of interest (ROI) prior to surgery. By allowing the study of growth, structure, and behaviour of the ROI in the planning phase, critical information can be obtained, increasing the likelihood of a successful operation. Usually, segmentations are performed manually or via machine learning methods trained on manual annotations. In contrast, this paper proposes a fully automatic, unsupervised inpainting-based brain tumour segmentation system for T1-weighted MRI. First, a deep convolutional neural network (DCNN) is trained to reconstruct missing healthy brain regions. Then, upon application, anomalous regions are determined by identifying areas of highest reconstruction loss. Finally, superpixel segmentation is performed to segment those regions. We show the proposed system is able to segment various sized and abstract tumours and achieves a mean and standard deviation Dice score of 0.771 and 0.176, respectively

Treatments and other prognostic factors in the management of the open abdomen: a systematic review

Purpose: The open abdomen (OA) is an important approach for managing intra-abdominal catastrophes and continues to be the standard of care. Despite this, challenges remain and the technique is still associated with a high incidence of complications and poor outcomes. A systematic review was performed to identify prognostic factors associated with OA management in relation to definitive fascial closure (DFC), mortality and intra-abdominal complications. Methodology: An electronic database search was conducted involving Medline, Excerpta Medica, Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature and Clinicaltrials.gov databases. Results: There were 31 studies included in the final synthesis. Prognostic factors associated with delaying DFC included the presence of deep surgical site infection, fascial necrosis or an intestinal fistula. Failed clearance of the abdomen, failure of fascial closure, unconsciousness and acute renal failure were associated with in-hospital mortality. Failed DFC, large bowel resection and administration of > 5-10 litres or > 10 litres of intravenous fluids in < 48 hours were associated with the development of entero-atmospheric fistula and/or intra-abdominal abscess. The source of infection (small bowel in relation to colon) was associated with the development of a ventral hernia. Fascial closure on or after day 5 or the presence of a bowel anastomosis were associated with the development of an anastomotic leak. Conclusion: The OA has earned a huge amount of popularity in recent decades. Careful selection and management of patients with an OA will aid in avoiding prolonged treatment and facilitate early DFC, decrease mortality and reduce intra-abdominal complications