Structural bias in population-based algorithms

Challenging optimisation problems are abundant in all areas of science and industry. Since the 1950s, scientists have responded to this by developing ever-diversifying families of 'black box' optimisation algorithms. The latter are designed to be able to address any optimisation problem, requiring only that the quality of any candidate solution can be calculated via a 'fitness function' specific to the problem. For such algorithms to be successful, at least three properties are required: (i) an effective informed sampling strategy, that guides the generation of new candidates on the basis of the fitnesses and locations of previously visited candidates; (ii) mechanisms to ensure efficiency, so that (for example) the same candidates are not repeatedly visited; and (iii) the absence of structural bias, which, if present, would predispose the algorithm towards limiting its search to specific regions of the solution space. The first two of these properties have been extensively investigated, however the third is little understood and rarely explored. In this article we provide theoretical and empirical analyses that contribute to the understanding of structural bias. In particular, we state and prove a theorem concerning the dynamics of population variance in the case of real-valued search spaces and a 'flat' fitness landscape. This reveals how structural bias can arise and manifest as non-uniform clustering of the population over time. Critically, theory predicts that structural bias is exacerbated with (independently) increasing population size, and increasing problem difficulty. These predictions, supported by our empirical analyses, reveal two previously unrecognised aspects of structural bias that would seem vital for algorithm designers and practitioners. Respectively, (i) increasing the population size, though ostensibly promoting diversity, will magnify any inherent structural bias, and (ii) the effects of structural bias are more apparent when faced with (many classes of) 'difficult' problems. Our theoretical result also contributes to the 'exploitation/exploration' conundrum in optimisation algorithm design, by suggesting that two commonly used approaches to enhancing exploration - increasing the population size, and increasing the disruptiveness of search operators - have quite distinct implications in terms of structural bias

GREEN-PSO: Conserving Function Evaluations in Particle Swarm Optimization

particle swarm optimization; swarm intelligence. In the Particle Swarm Optimization (PSO) algorithm, the expense of evaluating the objective function can make it difficult, or impossible, to use this approach effectively; reducing the number of necessary function evaluations would make it possible to apply the PSO algorithm more widely. Many function approximation techniques have been developed that address this issue, but an alternative to function approximation is function conservation. We describe GREEN-PSO (GR-PSO), an algorithm that, given a fixed number of function evaluations, conserves those function evaluations by probabilistically choosing a subset of particles smaller than the entire swarm on each iteration and allowing only those particles to perform function evaluations. The “surplus ” of function evaluations thus created allows a greater number of particles and/or iterations. In spite of the loss of information resulting from this more parsimonious use of function evaluations, GR-PSO performs as well as, or better than, the standard PSO algorithm on a set of six benchmark functions, both in terms of the rate of error reduction and the quality of the final solution.

A Simplified Recombinant PSO

Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding how a particle swarm optimization (PSO) swarm functions. One of these forms, PSO with discrete recombination, is extended and analyzed, demonstrating not just improvements in performance relative to a standard PSO algorithm, but also significantly different behavior, namely, a reduction in bursting patterns due to the removal of stochastic components from the update equations

Managed Care at the Crossroads: Can Managed Care Organizations Survive Government Regulation?

Attorneys Brown and Hartung provide a comprehensive overview of the development and structural components of managed health care plans. The article discusses the state regulatory controls affecting managed care including Patient Protection Acts. Mandated benefit provisions, any willing provider laws, and consumer access provisions. The article considers liability problems facing managed care organizations, in particular liabilities which arise from utilization and medical review discussions as well as gag clauses and financial incentive arrangements. The authors also review relevant federal regulatory initiatives

A novel abstraction for swarm intelligence: particle field optimization

Particle swarm optimization (PSO) is a popular meta-heuristic for black-box optimization. In essence, within this paradigm, the system is fully defined by a swarm of “particles” each characterized by a set of features such as its position, velocity and acceleration. The consequent optimized global best solution is obtained by comparing the personal best solutions of the entire swarm. Many variations and extensions of PSO have been developed since its creation in 1995, and the algorithm remains a popular topic of research. In this work we submit a new, abstracted perspective of the PSO system, where we attempt to move away from the swarm of individual particles, but rather characterize each particle by a field or distribution. The strategy that updates the various fields is akin to Thompson’s sampling. By invoking such an abstraction, we present the novel particle field optimization algorithm which harnesses this new perspective to achieve a model and behavior which is completely distinct from the family of traditional PSO system

Transcriptomic analysis of the poultry red mite, Dermanyssus gallinae, across all stages of the lifecycle

Acknowledgements Thanks go to the Centre for Genomic Research (CGR) at the University of Liverpool performing the TruSeq RNA-seq analysis and to our local layer farmers for their continued support and provision of mite material. Funding The authors gratefully acknowledge funding for this project from BBRSC (grant reference BB/J01513X/1), Zoetis and Akita Co. Ltd. and The British Egg Marketing Board Trust.Peer reviewedPublisher PD

Simple and Adaptive Particle Swarms

The substantial advances that have been made to both the theoretical and practical aspects of particle swarm optimization over the past 10 years have taken it far beyond its original intent as a biological swarm simulation. This thesis details and explains these advances in the context of what has been achieved to this point, as well as what has yet to be understood or solidified within the research community. Taking into account the state of the modern field, a standardized PSO algorithm is defined for benchmarking and comparative purposes both within the work, and for the community as a whole. This standard is refined and simplified over several iterations into a form that does away with potentially undesirable properties of the standard algorithm while retaining equivalent or superior performance on the common set of benchmarks. This refinement, referred to as a discrete recombinant swarm (PSODRS) requires only a single user-defined parameter in the positional update equation, and uses minimal additive stochasticity, rather than the multiplicative stochasticity inherent in the standard PSO. After a mathematical analysis of the PSO-DRS algorithm, an adaptive framework is developed and rigorously tested, demonstrating the effects of the tunable particle- and swarm-level parameters. This adaptability shows practical benefit by broadening the range of problems which the PSO-DRS algorithm is wellsuited to optimize

An Investigation of Factors Influencing Algorithm Selection for High Dimensional Continuous Optimisation Problems

The problem of algorithm selection is of great importance to the optimisation community, with a number of publications present in the Body-of-Knowledge. This importance stems from the consequences of the No-Free-Lunch Theorem which states that there cannot exist a single algorithm capable of solving all possible problems. However, despite this importance, the algorithm selection problem has of yet failed to gain widespread attention . In particular, little to no work in this area has been carried out with a focus on large-scale optimisation; a field quickly gaining momentum in line with advancements and influence of big data processing. As such, it is not as yet clear as to what factors, if any, influence the selection of algorithms for very high-dimensional problems (> 1000) - and it is entirely possible that algorithms that may not work well in lower dimensions may in fact work well in much higher dimensional spaces and vice-versa. This work therefore aims to begin addressing this knowledge gap by investigating some of these influencing factors for some common metaheuristic variants. To this end, typical parameters native to several metaheuristic algorithms are firstly tuned using the state-of-the-art automatic parameter tuner, SMAC. Tuning produces separate parameter configurations of each metaheuristic for each of a set of continuous benchmark functions; specifically, for every algorithm-function pairing, configurations are found for each dimensionality of the function from a geometrically increasing scale (from 2 to 1500 dimensions). The nature of this tuning is therefore highly computationally expensive necessitating the use of SMAC. Using these sets of parameter configurations, a vast amount of performance data relating to the large-scale optimisation of our benchmark suite by each metaheuristic was subsequently generated. From the generated data and its analysis, several behaviours presented by the metaheuristics as applied to large-scale optimisation have been identified and discussed. Further, this thesis provides a concise review of the relevant literature for the consumption of other researchers looking to progress in this area in addition to the large volume of data produced, relevant to the large-scale optimisation of our benchmark suite by the applied set of common metaheuristics. All work presented in this thesis was funded by EPSRC grant: EP/J017515/1 through the DAASE project

Leaders’ Perceptions of Innovation Processes in Public Sector Organizations

Successful innovation in the public sector has not yielded the intended results, thus the need exists for public sector leadership to foster innovation. This issue is important as the public sector represents up to 25% of a developed nation’s gross domestic product and is expected to deliver services efficiently. The purpose of this case study was to explore the skills public sector leaders need to foster innovation. The conceptual framework included organizational culture, motivation in innovative environments, implementation of innovation, and organizational relationships of Glor’s public sector organization theory, which helped consider minor or major challenges, intrinsic or extrinsic motivation, and whether its motivation is top-down or bottom-up. The research question focused on what skills public sector leaders need. The research design used a single case study approach. Data were collected using in-depth interviews with 15 public sector mid-level leaders. Data were analyzed via manual coding and theme development. Themes included: provide an opportunity for encouragement; do not be afraid to fail, internal fortitude; and manage leadership and political appointees as well. Providing a learning environment, accepting prudent risks, and providing structure and resources—keep people informed. Study’s results can inform public sector leaders to better understand the value of leadership for innovation and organizational culture for relationships affecting innovation, facilitating improved delivery of services to their respective populations, including leadership, employees, and the public

Equal but Different: Gender Discourses in the Social Relations of Irish Peacekeepers & Possibilities for Transformation

Equal but Different: Discourses in the Social Relations of Irish Peacekeepers & Possibilities for Transformation Motivated by the lack of action and transformation of gender relations within peacekeeping since the adoption of UNSCR 1325 in 2000, this study explores the gendering processes within the Irish Defence Forces that position women and men in particular roles informally and which act to support or inhibit women’s access to peacekeeping missions. Through discourse analysis this study reveals ‘equal but different’ as the dominant discourse on gender relations within the Irish Defence Forces and uses this discourse as a lens through which to assess the overarching question: ‘How does the “equal but different” discourse distribute power in different contexts and what impact does that have on women’s inclusion in PSOs?’ By drawing out the empirical data from the accounts of 28 women and men participants the findings reveal what women bring to a mission; inhibitors to their participation in missions; and transformative possibilities. The study’s major contribution is that it reveals multiple contradictory discourses depending on the context. Of particular importance to the feminist agenda is this study’s new empirical data on Irish peacekeepers and the development of critical alternative discourses on gender as a result of women’s presence in missions. These alternative discourses have the potential to transform gender relations by positioning women and men in the ‘third space’ which holds ‘equal, ‘different’ and ‘multiplicity’ of subjectivities simultaneously. This ‘third space’ creates a bridge between the liberal and critical feminist debates on women’s participation in peacekeeping, through its development of a new concept ‘add women and transform’. The ‘add women and transform’ concept is borne out of the empirical findings revealing how the presence of women in the military is leading to the creation of new critical discourses, and although they are muted, they have the potential to challenge unequal power dynamics within the military if they are supported by gender mainstreaming policies and a shift in peacekeeping practices