A Genetic Programming Based Heuristic to Simplify Rugged Landscapes Exploration

Some optimization problems are difficult to solve due to a considerable number of local optima, which may result in premature convergence of the optimization process. To address this problem, we propose a novel heuristic method for constructing a smooth surrogate model of the original function. The surrogate function is easier to optimize but maintains a fundamental property of the original rugged fitness landscape: the location of the global optimum. To create such a surrogate model, we consider a linear genetic programming approach coupled with a self-tuning fitness function. More specifically, to evaluate the fitness of the produced surrogate functions, we employ Fuzzy Self-Tuning Particle Swarm Optimization, a setting-free version of particle swarm optimization. To assess the performance of the proposed method, we considered a set of benchmark functions characterized by high noise and ruggedness. Moreover, the method is evaluated over different problems’ dimensionalities. The proposed approach reveals its suitability for performing the proposed task. In particular, experimental results confirm its capability to find the global argminimum for all the considered benchmark problems and all the domain dimensions taken into account, thus providing an innovative and promising strategy for dealing with challenging optimization problems. Doi: 10.28991/ESJ-2023-07-04-01 Full Text: PD

On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers

Pietropolli, G., Manzoni, L., Paoletti, A., & Castelli, M. (2023). On the Hybridization of Geometric Semantic GP with Gradient-based Optimizers. Genetic Programming And Evolvable Machines, 24(2 Special Issue on Highlights of Genetic Programming 2022 Events), 1-20. [16]. https://doi.org/10.21203/rs.3.rs-2229748/v1, https://doi.org/10.1007/s10710-023-09463-1---Open access funding provided by Università degli Studi di Trieste within the CRUI-CARE Agreement. 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 IMSGeometric semantic genetic programming (GSGP) is a popular form of GP where the effect of crossover and mutation can be expressed as geometric operations on a semantic space. A recent study showed that GSGP can be hybridized with a standard gradient-based optimized, Adam, commonly used in training artificial neural networks.We expand upon that work by considering more gradient-based optimizers, a deeper investigation of their parameters, how the hybridization is performed, and a more comprehensive set of benchmark problems. With the correct choice of hyperparameters, this hybridization improves the performances of GSGP and allows it to reach the same fitness values with fewer fitness evaluations.publishersversionepub_ahead_of_prin

Parametrizing GP Trees for Better Symbolic Regression Performance through Gradient Descent [Poster]

Pietropolli, G., Camerota verdù, F. J., Manzoni, L., & Castelli, M. (2023). Parametrizing GP Trees for Better Symbolic Regression Performance through Gradient Descent [Poster]. In S. Silva, & L. Paquete (Eds.), GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023 (pp. 619-622). Association for Computing Machinery (ACM). https://doi.org/10.1145/3583133.3590574Symbolic regression is a common problem in genetic programming (GP), but the syntactic search carried out by the standard GP algorithm often struggles to tune the learned expressions. On the other hand, gradient-based optimizers can efficiently tune parametric functions by exploring the search space locally. While there is a large amount of research on the combination of evolutionary algorithms and local search (LS) strategies, few of these studies deal with GP. To get the best from both worlds, we propose embedding learnable parameters in GP programs and combining the standard GP evolutionary approach with a gradient-based refinement of the individuals employing the Adam optimizer. We devise two different algorithms that differ in how these parameters are shared in the expression operators and report experimental results performed on a set of standard real-life application datasets. Our findings show that the proposed gradient-based LS approach can be effectively combined with GP to outperform the original algorithm.publishersversionpublishe

Significant relationship of combined ACP1/PTPN22 genotype variants with the growth of uterine leiomyomas

Objective: To analyze the interaction between ACP1 and PTPN22 concerning their effects on the growth of the tumor. In previous paper we have shown (i) that ACP1*B/*B genotype of ACP1 is negatively associated with the growth of leiomyomas and (ii) that there is a negative association of *C/*C genotype of PTPN22 with tumor growth. Materials and methods: Two hundred and three White women from the population of Rome with symptomatic leiomyomas were recruited in the University of Rome Tor Vergata. All subjects gave consent for the participation in the study that was approved by the Council of Department. ACP1 and PTPN22 genotypes were determined by DNA analysis. Results: The proportion of women with small leiomyomas decreases with the decrease of the number of protective factors and it is 37.2% in women carrying the joint genotype ACP1*B/*B-PTPN22 *C/*C (two protective factors) and 0% in women carrying no protective factors. Three way contingency table analysis by a log linear model has shown no evidence of epistatic interaction between the two genetic systems but a highly significant cooperative effect on the dimension of leiomyomas. There is a highly significant negative correlation between the number of protective factors and the dimension of leiomyomas with a minimum (cm 4.74) in women carrying the joint genotype ACP1*B/B-PTPN22 *C/*C and a maximum (cm 7.25) in women carrying no protective factors. Conclusion: The present study suggests a cooperative interaction between ACP1 and PTPN22 concerning their effects on the growth of uterine leiomyomas. The determination of the genotype of the two systems may help to evaluate the risk of clinical manifestations of this common benign tumor. Keywords: ACP1, PTPN22, Uterine leiomyoma

The genetics of feto-placental development: A study of acid phosphatase locus 1 and adenosine deaminase polymorphisms in a consecutive series of newborn infants

<p>Abstract</p> <p>Background</p> <p>Acid phosphatase locus 1 and adenosine deaminase locus 1 polymorphisms show cooperative effects on glucose metabolism and immunological functions. The recent observation of cooperation between the two systems on susceptibility to repeated spontaneous miscarriage prompted us to search for possible interactional effects between these genes and the correlation between birth weight and placental weight. Deviation from a balanced development of the feto-placental unit has been found to be associated with perinatal morbidity and mortality and with cardiovascular diseases in adulthood.</p> <p>Methods</p> <p>We examined 400 consecutive newborns from the Caucasian population of Rome. Birth weight, placental weight, and gestational length were registered. Acid phosphatase locus 1 and adenosine deaminase locus 1 phenotypes were determined by starch gel electrophoresis and correlation analysis was performed by SPSS programs. Informed verbal consent to participate in the study was obtained from the mothers.</p> <p>Results</p> <p>Highly significant differences in birth weight-placental weight correlations were observed among acid phosphatase locus 1 phenotypes (p = 0.005). The correlation between birth weight and placental weight was markedly elevated in subjects carrying acid phosphatase locus 1 phenotypes with medium-low F isoform concentration (A, CA and CB phenotypes) compared to those carrying acid phosphatase locus 1 phenotypes with medium-high F isoform concentration (BA and B phenotypes) (p = 0.002). Environmental and developmental variables were found to exert a significant effect on birth weight-placental weight correlation in subjects with medium-high F isoform concentrations, but only a marginal effect was observed in those with medium-low F isoform concentrations. The correlation between birth weight and placental weight is higher among carriers of the adenosine deaminase locus 1 allele*2, which is associated with low activity, than in homozygous adenosine deaminase locus 1 phenotype 1 carriers (p = 0.04). The two systems show a cooperative effect on the correlation between birth weight and placental weight: the highest value is observed in newborns carrying adenosine deaminase locus 1 allele*2 and acid phosphatase locus 1 phenotypes with medium-low F isoform concentration (p = 0.005).</p> <p>Conclusion</p> <p>These data suggest that zygotes with low adenosine deaminase locus 1 activity and low F activity may experience the most favourable intrauterine conditions for a balanced development of the feto-placental unit.</p

Studio di un modello di genetica spaziale

L'argomento di interesse del seguente testo e' quello dello studio di una po- polazione e della sua evoluzione dopo il susseguirsi delle generazioni, sia analizzando il problema da un punto di vista fortemente genetico, sia modellizzandolo e studiando l'evoluzione di questo tramite modelli fisico-matematici. In primo luogo, dunque, citeremo e analizzeremo brevemente una classi- ca legge della genetica, ovvero la legge di Hardy-Weinberg, studiando la relazioni tra i genotipi e le frequenze degli alleli in popolazioni dove l'accop- piamento sia casuale e dove non intervengano fenomeni esterni. Dopo una breve introduzione atta a fornire i preliminari matematici e le notazioni utili alla trattazione, introdurremo dei modelli spaziali relativi al- la popolazione delle piante, investigando sul comportamento di vari modelli di dispersione dei semi, che chiameremo figlie, rispetto alle piante che li ha no prodotti, che chiameremo madri. Il nostro obiettivo sara' quello di cerare un modello per cui il processo rag- giunga un equilibrio ed evitando la divergenza della popolazione in un unico punto

PPCon 1.0: Biogeochemical Argo Profile Prediction with 1D Convolutional Networks

Python implementation for paper "PPCon 1.0: Biogeochemical Argo Profile Prediction with 1D Convolutional Networks": Gloria Pietropolli, Luca Manzoni, and Gianpiero Cossarini. The code for running the model is contained in the PPCon.zip folder, the instructions for running the code are contained in the README.md file. The dataset are already generated in a tensor form ready for the training, and splitted into train and test. The datasets are contained in the ds.tar.gz folder. The GitHub repo for the paper is available here

Combining Genetic Programming and Particle Swarm Optimization to Simplify Rugged Landscapes Exploration

Most real-world optimization problems are difficult to solve with traditional statistical techniques or with metaheuristics. The main difficulty is related to the existence of a considerable number of local optima, which may result in the premature convergence of the optimization process. To address this problem, we propose a novel heuristic method for constructing a smooth surrogate model of the original function. The surrogate function is easier to optimize but maintains a fundamental property of the original rugged fitness landscape: the location of the global optimum. To create such a surrogate model, we consider a linear genetic programming approach enhanced by a self-tuning fitness function. The proposed algorithm, called the GP-FST-PSO Surrogate Model, achieves satisfactory results in both the search for the global optimum and the production of a visual approximation of the original benchmark function (in the 2-dimensional case)

Multivariate Relationship in Big Data Collection of Ocean Observing System

Observing the ocean provides us with essential information necessary to study and understand marine ecosystem dynamics, its evolution and the impact of human activities. However, observations are sparse, limited in time and space coverage, and unevenly collected among variables. Our work aims to develop an improved deep-learning technique for predicting relationships between high-frequency and low-frequency sampled variables. Specifically, we use a larger dataset, EMODnet, and train our model for predicting nutrient concentrations and carbonate system variables (low-frequency sampled variables) starting from information such as sampling time and geolocation, temperature, salinity and oxygen (high-frequency sampled variables). Novel elements of our application include (i) the calculation of a confidence interval for prediction based on deep ensembles of neural networks, and (ii) a two-step analysis for the quality check of the input data. The proposed method proves capable of predicting the desired variables with relatively small errors, outperforming the results obtained by the current state-of-the-art models

Combining Geometric Semantic GP with Gradient-Descent Optimization

Pietropolli, G., Manzoni, L., Paoletti, A., & Castelli, M. (2022). Combining Geometric Semantic GP with Gradient-Descent Optimization. In E. Medvet, G. Pappa, & B. Xue (Eds.), Genetic Programming. EuroGP 2022: 25th European Conference, EuroGP 2022 Held as Part of EvoStar 2022 Madrid, Spain, April 20–22, 2022 Proceedings (pp. 19-33). (Lecture Notes in Computer Science; Vol. 13223). Springer. https://doi.org/10.1007/978-3-031-02056-8_2Geometric semantic genetic programming (GSGP) is a well-known variant of genetic programming (GP) where recombination and mutation operators have a clear semantic effect. Both kind of operators have randomly selected parameters that are not optimized by the search process. In this paper we combine GSGP with a well-known gradient-based optimizer, Adam, in order to leverage the ability of GP to operate structural changes of the individuals with the ability of gradient-based methods to optimize the parameters of a given structure. Two methods, named HYB-GSGP and HeH-GSGP, are defined and compared with GSGP on a large set of regression problems, showing that the use of Adam can improve the performance on the test set. The idea of merging evolutionary computation and gradient-based optimization is a promising way of combining two methods with very different – and complementary – strengths.authorsversionpublishe