6 research outputs found
Full-Reference Image Quality Expression via Genetic Programming
Bakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2023). Full-Reference Image Quality Expression via Genetic Programming. IEEE Transactions on Image Processing, 32, 1458-1473. https://doi.org/10.1109/TIP.2023.3244662
This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) under the projects Algoritmos de Inteligência artificial no Consumo de crédito e conciliação de Endividamento (AICE) (DSAIPA/DS/0113/2019) and UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures.authorsversionauthorsversionpublishe
Semantic Segmentation Network Stacking with Genetic Programming
Bakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2023). Semantic Segmentation Network Stacking with Genetic Programming. Genetic Programming And Evolvable Machines, 24(2 — Special Issue on Highlights of Genetic Programming 2022 Events), 1-37. [15]. https://doi.org/10.1007/s10710-023-09464-0---Open access funding provided by FCT|FCCN (b-on). This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the projects GADgET (DSAIPA/DS/0022/2018), AICE (DSAIPA/DS/0113/2019), UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS, and by the grant SFRH/BD/137277/2018.Semantic segmentation consists of classifying each pixel of an image and constitutes an essential step towards scene recognition and understanding. Deep convolutional encoder–decoder neural networks now constitute state-of-the-art methods in the field of semantic segmentation. The problem of street scenes’ segmentation for automotive applications constitutes an important application field of such networks and introduces a set of imperative exigencies. Since the models need to be executed on self-driving vehicles to make fast decisions in response to a constantly changing environment, they are not only expected to operate reliably but also to process the input images rapidly. In this paper, we explore genetic programming (GP) as a meta-model that combines four different efficiency-oriented networks for the analysis of urban scenes. Notably, we present and examine two approaches. In the first approach, we represent solutions as GP trees that combine networks’ outputs such that each output class’s prediction is obtained through the same meta-model. In the second approach, we propose representing solutions as lists of GP trees, each designed to provide a unique meta-model for a given target class. The main objective is to develop efficient and accurate combination models that could be easily interpreted, therefore allowing gathering some hints on how to improve the existing networks. The experiments performed on the Cityscapes dataset of urban scene images with semantic pixel-wise annotations confirm the effectiveness of the proposed approach. Specifically, our best-performing models improve systems’ generalization ability by approximately 5% compared to traditional ensembles, 30% for the less performing state-of-the-art CNN and show competitive results with respect to state-of-the-art ensembles. Additionally, they are small in size, allow interpretability, and use fewer features due to GP’s automatic feature selection.publishersversionepub_ahead_of_prin
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
Ensemble learning with GSGP
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThe purpose of this thesis is to conduct comparative research between Genetic Programming
(GP) and Geometric Semantic Genetic Programming (GSGP), with different
initialization (RHH and EDDA) and selection (Tournament and Epsilon-Lexicase)
strategies, in the context of a model-ensemble in order to solve regression optimization
problems.
A model-ensemble is a combination of base learners used in different ways to solve
a problem. The most common ensemble is the mean, where the base learners are combined
in a linear fashion, all having the same weights. However, more sophisticated
ensembles can be inferred, providing higher generalization ability.
GSGP is a variant of GP using different genetic operators. No previous research has
been conducted to see if GSGP can perform better than GP in model-ensemble learning.
The evolutionary process of GP and GSGP should allow us to learn about the strength
of each of those base models to provide a more accurate and robust solution. The
base-models used for this analysis were Linear Regression, Random Forest, Support
Vector Machine and Multi-Layer Perceptron. This analysis has been conducted using 7
different optimization problems and 4 real-world datasets. The results obtained with
GSGP are statistically significantly better than GP for most cases.O objetivo desta tese é realizar pesquisas comparativas entre Programação Genética
(GP) e Programação Genética Semântica Geométrica (GSGP), com diferentes estratégias
de inicialização (RHH e EDDA) e seleção (Tournament e Epsilon-Lexicase), no
contexto de um conjunto de modelos, a fim de resolver problemas de otimização de
regressão.
Um conjunto de modelos é uma combinação de alunos de base usados de diferentes
maneiras para resolver um problema. O conjunto mais comum é a média, na qual
os alunos da base são combinados de maneira linear, todos com os mesmos pesos.
No entanto, conjuntos mais sofisticados podem ser inferidos, proporcionando maior
capacidade de generalização.
O GSGP é uma variante do GP usando diferentes operadores genéticos. Nenhuma
pesquisa anterior foi realizada para verificar se o GSGP pode ter um desempenho
melhor que o GP no aprendizado de modelos. O processo evolutivo do GP e GSGP
deve permitir-nos aprender sobre a força de cada um desses modelos de base para
fornecer uma solução mais precisa e robusta. Os modelos de base utilizados para esta
análise foram: Regressão Linear, Floresta Aleatória, Máquina de Vetor de Suporte e
Perceptron de Camadas Múltiplas. Essa análise foi realizada usando 7 problemas de
otimização diferentes e 4 conjuntos de dados do mundo real. Os resultados obtidos
com o GSGP são estatisticamente significativamente melhores que o GP na maioria
dos casos
Supporting medical decisions for treating rare diseases through genetic programming
Bakurov, I., Castelli, M., Vanneschi, L., & Freitas, M. J. (2019). Supporting medical decisions for treating rare diseases through genetic programming. In P. Kaufmann, & P. A. Castillo (Eds.), Applications of Evolutionary Computation: 22nd International Conference, EvoApplications 2019, Held as Part of EvoStar 2019, Proceedings (pp. 187-203). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11454 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-16692-2_13. ISBN: 978-3-030-16691-5; Online ISBN: 978-3-030-16692-2Casa dos Marcos is the largest specialized medical and residential center for rare diseases in the Iberian Peninsula. The large number of patients and the uniqueness of their diseases demand a considerable amount of diverse and highly personalized therapies, that are nowadays largely managed manually. This paper aims at catering for the emergent need of efficient and effective artificial intelligence systems for the support of the everyday activities of centers like Casa dos Marcos. We present six predictive data models developed with a genetic programming based system which, integrated into a web-application, enabled data-driven support for the therapists in Casa dos Marcos. The presented results clearly indicate the usefulness of the system in assisting complex therapeutic procedures for children suffering from rare diseases.authorsversionpublishe
an improvement of the evolutionary demes despeciation algorithm
Bakurov, I., Vanneschi, L., Castelli, M., & Fontanella, F. (2018). EDDA-V2: an improvement of the evolutionary demes despeciation algorithm. In Parallel Problem Solving from Nature – PPSN XV: 15th International Conference, 2018, Proceedings (pp. 185-196). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11101 LNCS). Springer Verlag. DOI: 10.1007/978-3-319-99253-2_15For any population-based algorithm, the initialization of the population is a very important step. In Genetic Programming (GP), in particular, initialization is known to play a crucial role - traditionally, a wide variety of trees of various sizes and shapes are desirable. In this paper, we propose an advancement of a previously conceived Evolutionary Demes Despeciation Algorithm (EDDA), inspired by the biological phenomenon of demes despeciation. In the pioneer design of EDDA, the initial population is generated using the best individuals obtained from a set of independent subpopulations (demes), which are evolved for a few generations, by means of conceptually different evolutionary algorithms - some use standard syntax-based GP and others use a semantics-based GP system. The new technique we propose here (EDDA-V2), imposes more diverse evolutionary conditions - each deme evolves using a distinct random sample of training data instances and input features. Experimental results show that EDDA-V2 is a feasible initialization technique: populations converge towards solutions with comparable or even better generalization ability with respect to the ones initialized with EDDA, by using significantly reduced computational time.authorsversionpublishe