A Study of Dynamic Populations in Geometric Semantic Genetic Programming

Farinati, D., Bakurov, I., & Vanneschi, L. (2023). A Study of Dynamic Populations in Geometric Semantic Genetic Programming. Information Sciences, 648(November), 1-21. [119513]. https://doi.org/10.1016/j.ins.2023.119513 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.Allowing the population size to variate during the evolution can bring advantages to evolutionary algorithms (EAs), retaining computational effort during the evolution process. Dynamic populations use computational resources wisely in several types of EAs, including genetic programming. However, so far, a thorough study on the use of dynamic populations in Geometric Semantic Genetic Programming (GSGP) is missing. Still, GSGP is a resource-greedy algorithm, and the use of dynamic populations seems appropriate. This paper adapts algorithms to GSGP to manage dynamic populations that were successful for other types of EAs and introduces two novel algorithms. The novel algorithms exploit the concept of semantic neighbourhood. These methods are assessed and compared through a set of eight regression problems. The results indicate that the algorithms outperform standard GSGP, confirming the suitability of dynamic populations for GSGP. Interestingly, the novel algorithms that use semantic neighbourhood to manage variation in population size are particularly effective in generating robust models even for the most difficult of the studied test problems.publishersversionpublishe

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Becomings or fixity? Intersectional challenges to reductive power relations

This paper examines the notion of acceleration as simultaneously dynamic and fast moving but underpinned by legacies from an earlier age that inform their development and the ways in which they inflect social life. It shows how sites of dynamic social acceleration can shift and change its focus over time, while (implicitly) maintaining the same logic of unequal power relations. In order to produce social justice and equality, it is, therefore, necessary to understand the logic and ideologies that underpin social relations and technological developments. The paper starts by illustrating the ways in which social acceleration is both longstanding and constitute ideologies of their time. It then considers the thinking of the UK psychologist Francis Galton, the cousin of Charles Darwin, and the legacy of his work. The third section presents the theoretical resources on which the paper draws. The paper then considers three examples of measurements that reproduce unequal power relations by fixing inequalities in their assumptions, even though they exemplify social acceleration. The three examples are parenting styles, unconscious bias and algorithms. The final main part of the paper considers possibilities for change by briefly historicising statistics and considering how they can be rethought. It also briefly discusses insider resistance to ideological fixity that reproduces and amplifies social inequalities of, for example, racialisation, gender and social class

Becomings or fixity?

This paper examines the notion of acceleration as simultaneously dynamic and fast moving but underpinned by legacies from an earlier age that inform their development and the ways in which they inflect social life. It shows how sites of dynamic social acceleration can shift and change its focus over time, while (implicitly) maintaining the same logic of unequal power relations. In order to produce social justice and equality, it is, therefore, necessary to understand the logic and ideologies that underpin social relations and technological developments. The paper starts by illustrating the ways in which social acceleration is both longstanding and constitute ideologies of their time. It then considers the thinking of the UK psychologist Francis Galton, the cousin of Charles Darwin, and the legacy of his work. The third section presents the theoretical resources on which the paper draws. The paper then considers three examples of measurements that reproduce unequal power relations by fixing inequalities in their assumptions, even though they exemplify social acceleration. The three examples are parenting styles, unconscious bias and algorithms. The final main part of the paper considers possibilities for change by briefly historicising statistics and considering how they can be rethought. It also briefly discusses insider resistance to ideological fixity that reproduces and amplifies social inequalities of, for example, racialisation, gender and social class

Computational statistics using the Bayesian Inference Engine

This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimised software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organise and reuse expensive derived data. The BIE is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the BIE emphasises hybrid tempered MCMC schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE is implements a full persistence or serialisation system that stores the full byte-level image of the running inference and previously characterised posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the BIE, enable model comparison for complex models and data sets. Finally, the BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU GPL.Comment: Resubmitted version. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU GP

The use of artificial intelligence and automatic remote monitoring for mosquito surveillance

Mosquito surveillance consists in the routine monitoring of mosquito populations: to determine the presence/absence of certain mosquito species; to identify changes in the abundance and/or composition of mosquito populations; to detect the presence of invasive species; to screen for mosquito-borne pathogens; and, finally, to evaluate the effectiveness of control measures. This kind of surveillance is typically performed by means of traps, which are regularly collected and manually inspected by expert entomologists for the taxonomical identification of the samples. The main problems with traditional surveillance systems are the cost in terms of time and human resources and the lag that is created between the time the trap is placed and collected. This lag can be crucial for the accurate time monitoring of mosquito population dynamics in the field, which is determinant for the precise design and implementation of risk assessment programs. New perspectives in this field include the use of smart traps and remote monitoring systems, which generate data completely interoperable and thus available for the automatic running of prediction models; the performance of risk assessments; the issuing of warnings; and the undertaking of historical analyses of infested areas. In this way, entomological surveillance could be done automatically with unprecedented accuracy and responsiveness, overcoming the problem of manual inspection labour costs. As a result, disease vector species could be detected earlier and with greater precision, enabling an improved control of outbreaks and a greater protection from diseases, thereby saving lives and millions of Euros in health costs.info:eu-repo/semantics/publishedVersio