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The inference of gene regulatory networks from expression data is an important area of research that provides insight to the inner workings of a biological system. In this paper we present a new reverse-engineering framework for gene regulatory network reconstruction. It exploits some interesting characteristics of genetic programming, like the ability of solving complex regression problems with little or no information about the underlying data and the ability of performing an automatic selection of features at learning time. Our framework not only reconstructs the topology of the network, but it is also able to simulate its dynamic behaviour, forecasting the gene expression values in the timepoints subsequent to the initial one. In this paper, we test the proposed framework on the well-known IRMA gene regulatory network, and we show that its performances are promising.

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. authorsversion authorsversion published

Bakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2022). Structural similarity index (SSIM) revisited: A data-driven approach. Expert Systems with Applications, 189, 1-19. [116087]. [Advanced online publication on 27 October 2021]. https://doi.org/10.1016/j.eswa.2021.116087--------------Funding Information: This work was supported by national funds through the FCT (Funda??o para a Ci?ncia e a Tecnologia), Portugal by the projects GADgET (DSAIPA/DS/0022/2018), BINDER (PTDC/CCIINF/29168/2017), and AICE (DSAIPA/DS/0113/2019). Mauro Castelli acknowledges the financial support from the Slovenian Research Agency, Slovenia (research core funding no. P5-0410). Several contemporaneous image processing and computer vision systems rely upon the full-reference image quality assessment (IQA) measures. The single-scale structural similarity index (SS-SSIM) is one of the most popular measures, and it owes its success to the mathematical simplicity, low computational complexity, and implicit incorporation of Human Visual System’s (HVS) characteristics. In this paper, we revise the original parameters of SSIM and its multi-scale counterpart (MS-SSIM) to increase their correlation with subjective evaluation. More specifically, we exploit the evolutionary computation and the swarm intelligence methods on five popular IQA databases, two of which are dedicated distance-changed databases, to determine the best combination of parameters efficiently. Simultaneously, we explore the effect of different scale selection approaches in the context of SS-SSIM. The experimental results show that with a proper fine-tuning (1) the performance of SS-SSIM and MS-SSIM can be improved, in average terms, by 8% and by 3%, respectively, (2) the SS-SSIM after the so-called standard scale selection achieves similar performance as if applying computationally more expensive state-of-the-art scale selection methods or MS-SSIM; moreover, (3) there is evidence that the parameters learned on a given database can be successfully transferred to other (previously unseen) databases; finally, (4) we propose a new set of reference parameters for SSIM’s variants and provide their interpretation. authorsversion published

Manzoni, L., Vanneschi, L., & Mauri, G. (2012). A distance between populations for one-point crossover in genetic algorithms. Theoretical Computer Science, 429, 213-221. https://doi.org/10.1016/j.tcs.2011.12.041 Genetic algorithms use transformation operators on the genotypic structures of the individuals to carry out a search. These operators define a neighborhood. To analyze various dynamics of the search process, it is often useful to define a distance in this space. In fact, using an operator-based distance can make the analysis more accurate and reliable than using distances which have no relationship with the genetic operators. In this paper we define a distance which is based on the standard one-point crossover. Given that the population strongly affects the neighborhood induced by the crossover, we first define a crossover-based distance between populations. Successively, we show that it is naturally possible to derive from this function a family of distances between individuals. Finally, we also introduce an algorithm to compute this distance efficiently. published

We present four new parallel and distributed particle swarm optimization methods consisting in a genetic algorithm whose individuals are co-evolving swarms, an “island model”-based multi-swarm system, where swarms are independent and interact by means of particle migrations at regular time steps, and their respective variants enriched by adding a repulsive component to the particles. We study the proposed methods on a wide set of problems including theoretically hand-tailored benchmarks and complex real-life applications from the field of drug discovery, with a particular focus on the generalization ability of the obtained solutions. We show that the proposed repulsive multi-swarm system has a better optimization ability than all the other presented methods on all the studied problems. Interestingly, the proposed repulsive multi-swarm system is also the one that returns the most general solutions.

Statistical methods, and in particular Machine Learning, have been increasingly used in the drug development workflow to accelerate the discovery phase and to eliminate possible failures early during clinical developments. In the past, the authors of this paper have been working specifically on two problems: (i) prediction of drug induced toxicity and (ii) evaluation of the target-drug chemical interaction based on chemical descriptors. Among the numerous existing Machine Learning methods and their application to drug development ( see for instance [ F. Yoshida, J.G. Topliss, QSAR model for drug human oral bioavailability, Journal of Medicinal Chemistry 43 (2000) 2575-2585; Frohlich, J. Wegner, F. Sieker, A. Zell, Kernel functions for attributed molecular graphs-a new similarity based approach to ADME prediction in classification and regression, QSAR and Combinatorial Science, 38( 4) ( 2003) 427431; C. W. Andrews, L. Bennett, L. X. Yu, Predicting human oral bioavailability of a compound: development of a novel quantitative structure-bioavailability relationship, Pharmacological Research 17 ( 2000) 639-644; J Feng, L. Lurati, H. Ouyang, T. Robinson, Y. Wang, S. Yuan, S. S. Young, Predictive toxicology: benchmarking molecular descriptors and statistical methods, Journal of Chemical Information Computer Science 43 ( 2003) 1463-1470; T. M. Martin, D. M. Young, Prediction of the acute toxicity (96-h LC50) of organic compounds to the fat head minnow (Pimephales promelas) using a group contribution method, Chemical Research in Toxicology 14( 10) ( 2001) 1378-1385; G. Colmenarejo, A. Alvarez-Pedraglio, J. L. Lavandera, Chemoinformatic models to predict binding affinities to human serum albumin, Journal of Medicinal Chemistry 44 ( 2001) 4370-4378; J. Zupan, P. Gasteiger, Neural Networks in Chemistry and Drug Design: An Introduction, 2nd edition, Wiley, 1999]), we have been specifically concerned with Genetic Programming. A first paper [F. Archetti, E. Messina, S. Lanzeni, L. Vanneschi, Genetic programming for computational pharmacokinetics in drug discovery and development, Genetic Programming and Evolvable Machines 8( 4) ( 2007) 17-26] has been devoted to problem ( i). The present contribution aims at developing a Genetic Programming based framework on which to build specific strategies which are then shown to be a valuable tool for problem ( ii). In this paper, we use target estrogen receptor molecules and genistein based drug compounds. Being able to precisely and efficiently predict their mutual interaction energy is a very important task: for example, it may have an immediate relationship with the efficacy of genistein based drugs in menopause therapy and also as a natural prevention of some tumors. We compare the experimental results obtained by Genetic Programming with the ones of a set of "non-evolutionary'' Machine Learning methods, including Support Vector Machines, Artificial Neural Networks, Linear and Least Square Regression. Experimental results confirm that Genetic Programming is a promising technique from the viewpoint of the accuracy of the proposed solutions, of the generalization ability and of the correlation between predicted data and correct ones. (C) 2009 Elsevier B. V. All rights reserved.

Shelves on which products are being displayed are one of the most important resources in retail environment. The decision of shelf-space allocation and management is therefore a critical issue in retail operation management. In this paper a hybrid algorithm that combines a genetic algorithm with a variable neighborhood search is proposed to address the shop shelf allocation problem. Results obtained from an extensive experimental phase show the suitability of the proposed algorithm in addressing the problem at hand.

Parameter estimation (PE) of biological systems is one of the most challenging problems in Systems Biology. Here we present a PE method that integrates particle swarm optimization (PSO) to estimate the value of kinetic constants, and a stochastic simulation algorithm to reconstruct the dynamics of the system. The fitness of candidate solutions, corresponding to vectors of reaction constants, is defined as the point-to-point distance between a simulated dynamics and a set of experimental measures, carried out using discrete-time sampling and various initial conditions. A multi-swarm PSO topology with different modalities of particles migration is used to account for the different laboratory conditions in which the experimental data are usually sampled. The whole method has been specifically designed and entirely executed on the GPU to provide a reduction of computational costs. We show the effectiveness of our method and discuss its performances on an enzymatic kinetics and a prokaryotic gene expression network.

Given the implicitly parallel nature of population-based heuristics, many contributions reporting on parallel and distributed models and implementations of these heuristics have appeared so far. They range from the most natural and simple ones, i.e. fitness-level embarrassingly parallel implementations (where, for instance, each candidate solution is treated as an independent agent and evaluated on a dedicated processor), to many more sophisticated variously interacting multi-population systems. In the last few years, researchers have dedicated a growing attention to Particle Swarm Optimization (PSO), a bio-inspired population based heuristic inspired by the behavior of flocks of birds and shoals of fish, given its extremely simple implementation and its high intrinsical parallelism. Several parallel and distributed models of PSO have been recently defined, showing interesting performances both on benchmarks and real-life applications. In this chapter we report on four parallel and distributed PSO methods that have recently been proposed. They consist in a genetic algorithm whose individuals are co-evolving swarms, an “island model”- based multi-swarm system, where swarms are independent and interact by means of particle migrations at regular time steps, and their respective variants enriched by adding a repulsive component to the particles. We show that the proposed repulsive multi-swarm system has a better optimization ability than all the other presented methods on a set of hand-tailored benchmarks and complex real-life applications.

Numerous studies can be found in literature concerning the idea of learning cellular automata (CA) rules that perform a given task by means of machine learning methods. Among these methods, genetic algorithms (GAs) have often been used with excellent results. Nevertheless, few attention has been dedicated so far to the generality and robustness of the learned rules. In this paper, we show that when GAs are used to evolve asynchronous one-dimensional CA rules, they are able to find more general and robust solutions compared to the more usual case of evolving synchronous CA rules.

We present a new reverse-engineering framework for gene regulatory network reconstruction. It works on temporal series of gene activation data and, using genetic programming, it extracts the activation functions of the different genes from those data. Successively, the gene regulatory network is reconstructed exploiting the automatic feature selection performed by genetic programming and its dynamics can be simulated using the previously extracted activation functions. The framework was tested on the well-known IRMA gene regulatory network, a simple network composed by five genes in the yeast Saccharomyces cerevisiae, defined in 2009 as a simplified biological model to benchmark reverse-engineering approaches. We show that the performances of the proposed framework on this benchmark network are encouraging.

The Sharpened No Free Lunch theorem states that all optimization algorithms have the same performance on sets of functions that are closed under permutation, independently of the considered performance measure. However, not all performance measures are informative on how fast or how accurately an algorithm can solve a given problem. In this paper we focus on a particular performance measure, called optimization speed, that quanti fies how fast a search algorithm is able to find an optimal solution, and we try to characterize the set of functions on which all possible search algorithms have the same optimization speed. We call fair these sets, and we prove some results about their structure, the number of such sets and the computational complexity of checking fairness.

The classification of cancer patients into risk classes is a very active field of research, with direct clinical applications. We have recently compared several machine learning methods on the well known 70-genes signature dataset. In that study, genetic programming showed promising results, given that it outperformed all the other techniques. Nevertheless, the study was preliminary, mainly because the validation dataset was preprocessed and all its features binarized in order to use logical operators for the genetic programming functional nodes. If this choice allowed simple interpretation of the solutions from the biological viewpoint, on the other hand the binarization of data was limiting, since it amounts to a sizable loss of information. The goal of this paper is to overcome this limitation, using the 70-genes signature dataset with real-valued expression data. The results we present show that genetic programming using the number of incorrectly classified instances as fitness function is not able to outperform the other machine learning methods. However, when a weighted average between false positives and false negatives is used to calculate fitness values, genetic programming obtains performances that are comparable with the other methods in the minimization of incorrectly classified instances and outperforms all the other methods in the minimization of false negatives, which is one of the main goals in breast cancer clinical applications. Also in this case, the solutions returned by genetic programming are simple, easy to understand, and they use a rather limited subset of the available features.

The goal of this paper is to present four new parallel and distributed particle swarm optimization methods and to experimentally compare their performances on a wide set of benchmark functions. These methods include a genetic algorithm whose individuals are co-evolving swarms, an "island model"-based multi-swarm system, where swarms are independent and they interact by means of particle migrations at regular time steps, and their respective variants enriched by adding a repulsive component to the particles. The benchmark functions used in our experimental study are two new sets of test functions (whose difficulty can be tuned by simply modifying the values of few real-valued parameters), the well known Rastrigin test functions, and the test functions proposed in the CEC 2005 numerical optimization competition. We show that the proposed repulsive multi-swarm system outperforms all the other presented methods. Copyright 2010 ACM.

We present a framework based on genetic algorithms to automatically generate cellular automata rules under four different asynchronous update models (fixed random sweep, random new sweep, clock and independent random ordering). We consider four different rules (18, 56, 110 and 180) with well known dynamics under synchronous update scheme. We try to reconstruct the same dynamics by means of a genetic algorithm using asynchronous update schemes. We show that in many cases it is impossible, by means of an asynchronous update scheme, to perfectly reconstruct these dynamics. Nevertheless, we show that the genetic algorithm finds the rules that more closely approximate the target behavior and the dynamics of the rules found by the genetic algorithm are rather similar to the target ones. In particular, we can always recognize a similar patter and we can also identify some differences in small details, which can be minimal (as for rule 18) or rather visible (as for rule 110). This paves the way to a deeper investigation on this track: does using asynchronous updates allow us to find more stable rules, i.e. rules that are less affected by noise, and thus do not overfit training data? This question remains open and answering it is one of the main goals of our current research.

Generalization is an important issue in colorimetric characterization of devices. We propose a framework based on Genetic Algorithms to select training samples from large datasets. Even though the framework is general, and can be used in principle for any dataset, we use two well known datasets as case studies: training samples are selected from the Macbeth ColorCheckerDC dataset and the trained models are tested on the Kodak Q60 photographic standard dataset. The presented experimental results show that the proposed framework has better, or at least comparable, performances than a set of other computational methods defined so far for the same goal (Hardeberg, Cheung, CIC and Schettini). Even more importantly, the proposed framework has the ability to optimize the training samples and the characterizing polynomial's coefficients at the same time. © 2010 Springer-Verlag Berlin Heidelberg.

The modelling of biochemical systems requires the knowledge of several quantitative parameters (e.g. reaction rates) which are often hard to measure in laboratory experiments. Furthermore, when the system involves small numbers of molecules, the modelling approach should also take into account the effects of randomness on the system dynamics. In this paper, we tackle the problem of estimating the unknown parameters of stochastic biochemical systems by means of two optimization heuristics, genetic algorithms and particle swarm optimization. Their performances are tested and compared on two basic kinetics schemes: the Michaelis-Menten equation and the Brussellator. The experimental results suggest that particle swarm optimization is a suitable method for this problem. The set of parameters estimated by particle swarm optimization allows us to reliably reconstruct the dynamics of the Michaelis-Menten system and of the Brussellator in the oscillating regime. © Springer-Verlag Berlin Heidelberg 2009.

The success of a drug treatment is strongly correlated with the ability of a molecule to reach its target in the patient's organism without inducing toxic effects. Moreover the reduction of cost and time associated with drug discovery and development is becoming a crucial requirement for pharmaceutical industry. Therefore computational methods allowing reliable predictions of newly synthesized compounds properties are of outmost relevance. In this paper we discuss the role of genetic programming in predictive pharmacokinetics, considering the estimation of adsorption, distribution, metabolism, excretion and toxicity processes (ADMET) that a drug undergoes into the patient's organism. We compare genetic programming with other well known machine learning techniques according to their ability to predict oral bioavailability (%F), median oral lethal dose (LD50) and plasma-protein binding levels (%PPB). Since these parameters respectively characterize the percentage of initial drug dose that effectively reaches the systemic blood circulation, the harmful effects and the distribution into the organism of a drug, they are essential for the selection of potentially good molecules. Our results suggest that genetic programming is a valuable technique for predicting pharmacokinetics parameters, both from the point of view of the accuracy and of the generalization ability.

International audience; Neutrality of some boolean parity fitness landscapes is investigated in this paper. Compared with some well known contributions on the same issue, we define some new measures that help characterizing neutral landscapes, we use a new sampling methodology, which captures some features that are disregarded by uniform random sampling, and we introduce new genetic operators to define the neighborhood of tree structures. We compare the fitness landscape induced by two different sets of functional operators (SNand and SXorNot). The different characteristics of the neutral networks seem to justify the different difficulties of these landscapes for genetic programming.

To analyse various properties of the search process of genetic programming it is useful to quantify the distance between two individuals. Using operator-based distance measures can make this analysis more accurate and reliable than using distance measures which have no relationship with the genetic operators. This paper extends a recent definition of a distance measure based on subtree crossover for genetic programming. Empirical studies are presented that show the suitability of this measure to dynamically calculate the fitness distance correlation coefficient during the evolution, to construct a fitness sharing system for genetic programming and to measure genotypic diversity in the population. These experiments confirm the accuracy of the new measure and its consistency with the subtree crossover genetic operator.

This paper presents a study of fitness distance correlation and negative slope coefficient as measures of problem hardness for genetic programming. Advantages and drawbacks of both these measures are presented both from a theoretical and empirical point of view. Experiments have been performed on a set of well-known hand-tailored problems and “real-life-like” GP benchmarks.

This paper presents an experimental study of distributed multipopulation genetic programming. Using three well-known benchmark problems and one real-life problem, we discuss the role of the parameters that characterize the evolutionary process of standard panmictic and parallel genetic programming. We find that distributing individuals between subpopulations offers in all cases studied here an advantage both in terms of the quality of solutions and of the computational effort spent, when compared to single populations. We also study the influence of communication patterns such as the communication topology, the number of individuals exchanged and the frequency of exchange on the evolutionary process. We empirically show that the topology does not have a marked influence on the results for the test cases studied here, while the frequency and number of individuals exchanged are related and there exists a suitable range for those parameters which is consistently similar for all the problems studied.

Cooperative coevolution has proven to be a promising technique for solving complex combinatorial optimization problems. In this paper, we present four different strategies which involve cooperative coevolution of a genetic program and of a population of constants evolved by a genetic algorithm. The genetic program evolves expressions that solve a problem, while the genetic algorithm provides "good" values for the numeric terminal symbols used by those expressions. Experiments have been performed on three symbolic regression problems and on a "real-world" biomedical application. Results are encouraging and confirm that our coevolutionary algorithms can be used effectively in different domains.

The paper presents an empirical study aiming at evaluating and comparing several Machine Learning (ML) classification techniques in the automatic recognition of known patterns. The main motivations of this work is to select best performing classification techniques where target classes are based on the occurrence of known patterns in configurations of a forest system modeled according to Cellular Automata. Best performing ML classifiers will be adopted for the study of ecosystem dynamics within an interdisciplinary research collaboration between computer scientists biologists and ecosystem managers (Cellular Automata For Forest Ecosystems - CAFFE project). One of the main aims of the CAFFE project is the development of an analysis method based on recognition in CA state configurations of spatial patterns whose interpretations are inspired by the Chinese Go game.

International audience; We define a set of measures that capture some different aspects of neutrality in evolutionary algorithms fitness landscapes from a qualitative point of view. If considered all together, these measures offer a rather complete picture of the characteristics of fitness landscapes bound to neutrality and may be used as broad indicators of problem hardness. We compare the results returned by these measures with the ones of negative slope coefficient, a quantitative measure of problem hardness that has been recently defined and with success rate statistics on a well known genetic programming benchmark: the multiplexer problem. In order to efficaciously study the search space, we use a sampling technique that has recently been introduced and we show its suitability on this problem.

Automatically assessing the value of bioavailability from the chemical structure of a molecule is a very important issue in biomedicine and pharmacology. In this paper, we present an empirical study of some well known Machine Learning techniques, including various versions of Genetic Programming, which have been trained to this aim using a dataset of molecules with known bioavailability. Genetic Programming has proven the most promising technique among the ones that have been considered both from the point of view of the accurateness of the solutions proposed, of the generalization capabilities and of the correlation between predicted data and correct ones. Our work represents a first answer to the demand for quantitative bioavailability estimation methods proposed in literature, since the previous contributions focus on the classification of molecules into classes with similar bioavailability.