Visualising the Landscape of Multi-Objective Problems using Local Optima Networks

This is the author accepted manuscript. The final version is available from ACM via the DOI in this recordThe codebase for this paper is available at https://github.com/fieldsend/mo_lonsLocal optima networks (LONs) represent the landscape of optimisation problems. In a LON, graph vertices represent local optima in the search domain, their radii the basin sizes, and directed edges between vertices the ability to transit from one basin to another (with the edge width denoting how easy this is). Recently, a network construction approach inspired by LONs has been proposed for multi-objective problems which uses an undirected graph, representing mutually non-dominating solutions and neighbouring links, but not basin sizes. In contrast, here we introduce two formulations for multi/many-objective problems which are analogous to the traditional LON, using dominance-based hill-climbing to characterise the search domain. Each vertex represents a set of locally optimal solutions, with basins and ease of transition between them shown. These LONs vary depending on whether a point-based (dominance neutral optima) or set-based (Pareto local optima) representation is used to define mode construction. We illustrate these alternative formulations on some illustrative problems.We discuss some of the underlying computational issues in constructing LONs in a multiobjective as opposed to uni-objective problem domain, along with the inherent issue of neutrality — as each a vertex in these graphs almost invariably represents a set in our proposed constructs.Engineering and Physical Sciences Research Council (EPSRC

Search algorithms for regression test case prioritization

Regression testing is an expensive, but important, process. Unfortunately, there may be insufficient resources to allow for the re-execution of all test cases during regression testing. In this situation, test case prioritisation techniques aim to improve the effectiveness of regression testing, by ordering the test cases so that the most beneficial are executed first. Previous work on regression test case prioritisation has focused on Greedy Algorithms. However, it is known that these algorithms may produce sub-optimal results, because they may construct results that denote only local minima within the search space. By contrast, meta-heuristic and evolutionary search algorithms aim to avoid such problems. This paper presents results from an empirical study of the application of several greedy, meta-heuristic and evolutionary search algorithms to six programs, ranging from 374 to 11,148 lines of code for 3 choices of fitness metric. The paper addresses the problems of choice of fitness metric, characterisation of landscape modality and determination of the most suitable search technique to apply. The empirical results replicate previous results concerning Greedy Algorithms. They shed light on the nature of the regression testing search space, indicating that it is multi-modal. The results also show that Genetic Algorithms perform well, although Greedy approaches are surprisingly effective, given the multi-modal nature of the landscape

Comparison-based algorithms: worst-case optimality, optimality w.r.t a bayesian prior, the intraclass-variance minimization in EDA, and implementations with billiards

This paper is centered on the analysis of comparison-based algorithms. It has been shown recently that these algorithms are at most linearly convergent with a constant 1 − O(1/d); we here show that these algorithms are however optimal for robust optimization w.r.t increasing transformations of the fitness. We then turn our attention to the design of optimal comparison-based algorithms. No-Free-Lunch theorems have shown that introducing priors is necessary in order to design algorithms better than others; therefore, we include a bayesian prior in the spirit of learning theory. We show that these algorithms have a nice interpretation in terms of Estimation-Of-Distribution algorithms, and provide tools for the optimal design of generations of lambda-points by the way of billiard algorithms

Prescriptive formalism for constructing domain-specific evolutionary algorithms

It has been widely recognised in the computational intelligence and machine learning communities that the key to understanding the behaviour of learning algorithms is to understand what representation is employed to capture and manipulate knowledge acquired during the learning process. However, traditional evolutionary algorithms have tended to employ a fixed representation space (binary strings), in order to allow the use of standardised genetic operators. This approach leads to complications for many problem domains, as it forces a somewhat artificial mapping between the problem variables and the canonical binary representation, especially when there are dependencies between problem variables (e.g. problems naturally defined over permutations). This often obscures the relationship between genetic structure and problem features, making it difficult to understand the actions of the standard genetic operators with reference to problem-specific structures. This thesis instead advocates m..

Multi-Objective Archiving

Most multi-objective optimisation algorithms maintain an archive explicitly or implicitly during their search. Such an archive can be solely used to store high-quality solutions presented to the decision maker, but in many cases may participate in the search process (e.g., as the population in evolutionary computation). Over the last two decades, archiving, the process of comparing new solutions with previous ones and deciding how to update the archive/population, stands as an important issue in evolutionary multi-objective optimisation (EMO). This is evidenced by constant efforts from the community on developing various effective archiving methods, ranging from conventional Pareto-based methods to more recent indicator-based and decomposition-based ones. However, the focus of these efforts is on empirical performance comparison in terms of specific quality indicators; there is lack of systematic study of archiving methods from a general theoretical perspective. In this paper, we attempt to conduct a systematic overview of multi-objective archiving, in the hope of paving the way to understand archiving algorithms from a holistic perspective of theory and practice, and more importantly providing a guidance on how to design theoretically desirable and practically useful archiving algorithms. In doing so, we also present that archiving algorithms based on weakly Pareto compliant indicators (e.g., epsilon-indicator), as long as designed properly, can achieve the same theoretical desirables as archivers based on Pareto compliant indicators (e.g., hypervolume indicator). Such desirables include the property limit-optimal, the limit form of the possible optimal property that a bounded archiving algorithm can have with respect to the most general form of superiority between solution sets.Comment: 21 pages, 4 figures, journa

Shaping bacterial population behavior through computer-interfaced control of individual cells

This is the final version. Available from Springer Nature via the DOI in this record.Strains and data are available from the authors upon request. Custom scripts for the described setup are available as Supplementary Software.Bacteria in groups vary individually, and interact with other bacteria and the environment to produce population-level patterns of gene expression. Investigating such behavior in detail requires measuring and controlling populations at the single-cell level alongside precisely specified interactions and environmental characteristics. Here we present an automated, programmable platform that combines image-based gene expression and growth measurements with on-line optogenetic expression control for hundreds of individual Escherichia coli cells over days, in a dynamically adjustable environment. This integrated platform broadly enables experiments that bridge individual and population behaviors. We demonstrate: (i) population structuring by independent closed-loop control of gene expression in many individual cells, (ii) cell-cell variation control during antibiotic perturbation, (iii) hybrid bio-digital circuits in single cells, and freely specifiable digital communication between individual bacteria. These examples showcase the potential for real-time integration of theoretical models with measurement and control of many individual cells to investigate and engineer microbial population behavior.European Union's Seventh Frame ProgrammeAustrian Science FundAgence Nationale de la RechercheAgence Nationale de la RechercheAgence Nationale de la Recherch

An aptamer-based sensing platform for luteinising hormone pulsatility measurement

Normal fertility in human involves highly orchestrated communication across the hypothalamic-pituitary-gonadal (HPG) axis. The pulsatile release of Luteinising Hormone (LH) is a critical element for downstream regulation of sex steroid hormone synthesis and the production of mature eggs. Changes in LH pulsatile pattern have been linked to hypothalamic dysfunction, resulting in multiple reproductive and growth disorders including Polycystic Ovary Syndrome (PCOS), Hypothalamic Amenorrhea (HA), and delayed/precocious puberty. Therefore, assessing the pulsatility of LH is important not only for academic investigation of infertility, but also for clinical decisions and monitoring of treatment. However, there is currently no clinically available tool for measuring human LH pulsatility. The immunoassay system is expensive and requires large volumes of patient blood, limiting its application for LH pulsatility monitoring. In this thesis, I propose a novel method using aptamer-enabled sensing technology to develop a device platform to measure LH pulsatility. I first generated a novel aptamer binding molecule against LH by a nitrocellulose membrane-based in vitro selection then characterised its high affinity and specific binding properties by multiple biophysical/chemical methods. I then developed a sensitive electrochemical-based detection method using this aptamer. The principal mechanism is that structure switching upon binding is associated with the electron transfer rate changes of the MB redox label. I then customised this assay to numerous device platforms under our rapid prototyping strategy including 96 well automated platform, continuous sensing platform and chip-based multiple electrode platform. The best-performing device was found to be the AELECAP (Automated ELEctroChemical Aptamer Platform) – a 96-well plate based automatic micro-wire sensing platform capable of measuring a series of low volume luteinising hormone within a short time. Clinical samples were evaluated using AELECAP. A series of clinical samples were measured including LH pulsatility profile of menopause female (high LH amplitude), normal female/male (normal LH amplitude) and female with hypothalamic amenorrhea (no LH pulsatility). Total patient numbers were 12 of each type, with 50 blood samples collected every 10 mins in 8 hours. Results showed that the system can distinguish LH pulsatile pattern among the cohorts and pulsatility profiles were consistent with the result measured by clinical assays. AELECAP shows high potential as a novel approach for clinical aptamer-based sensing. AELECAP competes with current automated immunometric assays system with lower costs, lower reagent use, and a simpler setup. There is potential for this approach to be further developed as a tool for infertility research and to assist clinicians in personalised treatment with hormonal therapy.Open Acces

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Swarm Robotic Systems with Minimal Information Processing

This thesis is concerned with the design and analysis of behaviors in swarm robotic systems using minimal information acquisition and processing. The motivation for this work is to contribute in paving the way for the implementation of swarm robotic systems at physically small scales, which will open up new application domains for their operation. At these scales, the space and energy available for the integration of sensors and computational hardware within the individual robots is at a premium. As a result, trade-offs in performance can be justified if a task can be achieved in a more parsimonious way. A framework is developed whereby meaningful collective behaviors in swarms of robots can be shown to emerge without the robots, in principle, possessing any run-time memory or performing any arithmetic computations. This is achieved by the robots having only discrete-valued sensors, and purely reactive controllers. Black-box search methods are used to automatically synthesize these controllers for desired collective behaviors. This framework is successfully applied to two canonical tasks in swarm robotics: self-organized aggregation of robots, and self-organized clustering of objects by robots. In the case of aggregation, the robots are equipped with one binary sensor, which informs them whether or not there is another robot in their line of sight. This makes the structure of the robots’ controller simple enough that its entire space can be systematically searched to locate the optimal controller (within a finite resolution). In the case of object clustering, the robots’ sensor is extended to have three states, distinguishing between robots, objects, and the background. This still requires no run-time memory or arithmetic computations on the part of the robots. It is statistically shown that the extension of the sensor to have three states leads to a better performance as compared to the cases where the sensor is binary, and cannot distinguish between robots and objects, or robots and the background