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International audience; Context Managing land use to promote an ecosystem service (ES) without reducing others is challenging. The spatial scale at which no-loss constraints are imposed is relevant.Objectives We examined the influence of the spatial scale of no-loss constraints on ESs when one ES was optimised. Specifically, we investigated how carbon sequestration could be maximized at different spatial scales in France with constraints of no-loss on other ESs.Methods We used a statistical model linking land use and land cover variables to ESs [carbon sequestration (CS), crop production (CP), livestock production, timber growth] in French small agricultural regions (SARs). We optimised CS at the country scale posing no-loss constraints on other ESs at increasing spatial scales, i.e., SARs (scenario 'SARs'), department ('DEP'), administrative region ('REG'), and France ("FRANCE"). We analysed differences between optimized and initial configurations.Results Optimized CS at the country scale increased with the spatial scale at which no-loss constraints were posed (similar to+ 0.51% for 'DEP' and similar to+ 2.05% for 'FRANCE'). The variability of ES variation among the SARs similarly increased. This suggested that constraints at larger scales lead to ES segregation. Correlations among ES variations changed with the scenarios (Spearman's rho between CS and CP was - 0.43 for 'DEP' and - 0.70 for 'FRANCE'). This indicated that different land use strategies produce different degrees of enhancement/softening of ES trade-offs/synergies. Conclusions A trade-off was highlighted: larger spatial scales promoted better performance of the target ES but also spatial inequality. We argue that addressing smaller scales will lead to land-sharing solutions that avoid the local environmental impacts of land-sparing strategies.

International audience; To meet current societal demand for more sustainable transformation processes and bioresources, these processes must be optimized and new ones developed. The evolution of various systems (raw material, food, or process attributes) can be predicted to optimize the uses of biomass for better quality, safety, economic benefit, and sustainability. Predictive modeling can guide the necessary changes and influence industrials, governmental policies and consumers decision-making. However, achieving good predictive capability requires reflection on the models and model validation, which can be difficult. This review aims to help scientists begin to predict by presenting the techniques currently used in predictive science for food and related bioproducts. First, a guideline helps readers initiate a prediction process along with final tips and a warning about the risks involved, with a particular focus on the crucial validation step. Threebroad categories of techniques are then presented: empirical, mechanistic, and artificial intelligence (or “data-driven”). For each category, the advantages and limitations of current techniques for prediction are explained in light of their current domains of applications, illustrated with literature studies and a detailed example. Thus this article provides engineering researchers information about predictive modeling which is a recent relevant development in optimization of both food and nonfood bioresources processes.Practical applications Predictive modeling is a recent development of much relevance in the optimizationof both food and nonfood bioresources processes. The goal of this article is to guide those in research or industry who would like to start predicting. Therefore, the article is intended as a primer on prediction concepts and predictive techniques for food and non-food bioresources processing. Three categories of techniques commonly used in these fields are illustrated by various examples of current applications and amore detailed example helps to understand the implementation process. An increased ability of the global scientific body to predict the outcome of various decisions, often linked or sequential, will open new avenues for designing food products with circularity in mind: maintaining value and not creating waste in the process.

Due to the relatively recent history of industrial insect farming, there are still no extensive studies on the optimization of insect production chains. In this context, the present work aims to be a first step in filling this gap. A tentative set of mathematical models is proposed to take into account three different, conflicting objectives: maximizing economic viability, minimizing environmental impacts, and maximizing societal benefits. The state-of-the-art multi-objective algorithm NSGA-II is used to obtain an approximate Pareto fronts of solutions, that are later analyzed to identify suitable trade-offs. While preliminary, the results are encouraging enough for the computer-assisted design and development of sustainable insect production chains. Future works will take into account more extensive models, able to simulate scenarios in different European countries, and include parts of the chain such as transportation of goods to and from the production facilities.

Gear backlash is a quite serious problem in industrial robots, it causes vibrations and impairs the robot positioning accuracy. Backlash estimation allows targeted maintenance interventions, preserving robot performances and avoiding unforeseen equipment breakdowns. However, a direct measure of the backlash is hard to obtain, and dedicated auxiliary sensors are required for the measurement. This paper presents a method for estimating backlash in robotic joints that does not require the installation of extra devices. It only relies on data gathered from the motor encoder, which is always present in a robotic joint. The approach is based on the observation of a characteristic vibration pattern arising on the motor speed signal when backlash affects the joint transmission. By looking at the amplitude of this vibration some information about the entity of the backlash in the joint is gathered. Experimental results on simulated data are reported in the study to show the robustness of the method, also with respect of noise. Furthermore, tests on real-world data, gathered from robots installed in a production plant, demonstrate the efficacy of the technique. The approach is cost-effective, fast, and easily automatable, therefore convenient for the industrial world.

International audience; Mathematical modelling plays an important role in food engineering having various mathematical models tailored for different food topics. However, mathematical models are followed by limited information on their application in food companies. This paper aims to discuss the extent and the conditions surrounding the usage of mathematical models in the context of European food and drinks industry. It investigates the knowledge, nature and current use of modelling approaches in relation to the industry main characteristics. A total of 203 food companies from 12 European countries were included in this research.Results reveal that the country where the company operates, and size of the company, are more important predictors on the usage of mathematical models followed by the type of food sector. The more developed countries are positioned at the higher level of knowledge and use of available models. Similar pattern was observed at the micro level showing that small or medium sized companies exhibit lack of knowledge, resources and limiting usage of models.

Exploiting the availability of the largest collection of patient-derived xenografts from metastatic colorectal cancer annotated for a response to therapies, this manuscript aims to characterize the biological phenomenon from a mathematical point of view. In particular, we design an experiment in order to investigate how genes interact with each other. By using a shallow neural network model, we find reduced feature subspaces where the resistance phenomenon may be much easier to understand and analyze.

Feature selection is the process of choosing, or removing, features to obtain the most informative feature subset of minimal size. Such subsets are used to improve performance of machine learning algorithms and enable human understanding of the results. Approaches to feature selection in literature exploit several optimization algorithms. Multi-objective methods also have been proposed, minimizing at the same time the number of features and the error. While most approaches assess error resorting to the average of a stochastic K-fold cross-validation, comparing averages might be misleading. In this paper, we show how feature subsets with different average error might in fact be non-separable when compared using a statistical test. Following this idea, clusters of non-separable optimal feature subsets are identified. The performance in feature selection can thus be evaluated by verifying how many of these optimal feature subsets an algorithm is able to identify. We thus propose a multi-objective optimization approach to feature selection, EvoFS, with the objectives to i. minimize feature subset size, ii. minimize test error on a 10-fold cross-validation using a specific classifier, iii. maximize the analysis of variance value of the lowest-performing feature in the set. Experiments on classification datasets whose feature subsets can be exhaustively evaluated show that our approach is able to always find the best feature subsets. Further experiments on a high-dimensional classification dataset, that cannot be exhaustively analyzed, show that our approach is able to find more optimal feature subsets than state-of-the-art feature selection algorithms.

In the field of artificial intelligence, agents learn how to take decisions by fitting their parameters on a set of samples called training set. Similarly, a core set is a subset of the training samples such that, if an agent exploits this set to fit its parameters instead of the whole training set, then the quality of the inferences does not change significantly. Relaxing the constraint that restricts the search for core sets to the available data, neural networks may be used to generate virtual samples, called archetype set, containing the same kind of information. This work illustrates the features of GH-ARCH, a recently proposed self-organizing hierarchical neural network for archetype discovery. Experiments show how the use of archetypes allows both ML agents to make fast and accurate predictions and human experts to make sense of such decisions by analyzing few important samples.

Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hidden information, uncertainty and planning, among other aspects. This paper proposes the use of evolutionary algorithms (EAs) to develop agents who play a card game, Hearthstone, by optimizing a data-driven decision-making mechanism that takes into account all the elements currently in play. Agents feature self-learning by means of a competitive coevolutionary training approach, whereby no external sparring element defined by the user is required for the optimization process. One of the agents developed through the proposed approach was runner-up (best 6%) in an international Hearthstone Artificial Intelligence (AI) competition. Our proposal performed remarkably well, even when it faced state-of-the-art techniques that attempted to take into account future game states, such as Monte-Carlo Tree search. This outcome shows how evolutionary computation could represent a considerable advantage in developing AIs for collectible card games such as Hearthstone.

In the field of machine learning, coresets are defined as subsets of the training set that can be used to obtain a good approximation of the behavior that a given algorithm would have on the whole training set. Advantages of using coresets instead of the training set include improving training speed and allowing for a better human understanding of the dataset. Not surprisingly, coreset discovery is an active research line, with several notable contributions in literature. Nevertheless, restricting the search for representative samples to the available data points might impair the final result. In this work, neural networks are used to create sets of virtual data points, named archetypes, with the objective to represent the information contained in a training set, in the same way a coreset does. Starting from a given training set, a hierarchical clustering neural network is trained and the weight vectors of the leaves are used as archetypes on which the classifiers are trained. Experimental results on several benchmarks show that the proposed approach is competitive with traditional coreset discovery techniques, delivering results with higher accuracy, and showing a greater ability to generalize to unseen test data.

International audience

This paper gives an overview of scientific challenges that occur when performing life-cycle assessment (LCA) in the food supply chain. In order to evaluate these risks, the Failure Mode and Effect Analysis tool has been used. Challenges related to setting the goal and scope of LCA revealed four hot spots: system boundaries of LCA; used functional units; type and quality of data categories, and main assumptions and limitations of the study. Within the inventory analysis, challenging issues are associated with allocation of material and energy flows and waste streams released to the environment. Impact assessment brings uncertainties in choosing appropriate environmental impacts. Finally, in order to interpret results, a scientifically sound sensitivity analysis should be performed to check how stable calculations and results are. Identified challenges pave the way for improving LCA of food supply chains in order to enable comparison of results.

In machine learning a coreset is defined as a subset of the training set using which an algorithm obtains performances similar to what it would deliver if trained over the whole original data. Advantages of coresets include improving training speed and easing human understanding. Coreset discovery is an open line of research as limiting the training might also impair the quality of the result. Differently, virtual points, here called archetypes, might be far more informative for a machine learning algorithm. Starting from this intuition, a novel evolutionary approach to archetype set discovery is presented: starting from a population seeded with candidate coresets, a multi-objective evolutionary algorithm is set to modify them and eventually create archetype sets, to minimize both number of points in the set and classification error. Experimental results on popular benchmarks show that the proposed approach is able to deliver results that allow a classifier to obtain lower error and better ability of generalizing on unseen data than state-of-the-art coreset discovery techniques.

In an optimization problem, a coreset can be defined as a subset of the input points, such that a good approximation to the optimization problem can be obtained by solving it directly on the coreset, instead of using the whole original input. In machine learning, coresets are exploited for applications ranging from speeding up training time, to helping humans understand the fundamental properties of a class, by considering only a few meaningful samples. The problem of discovering coresets, starting from a dataset and an application, can be defined as identifying the minimal amount of samples that do not significantly lower performance with respect to the performance on the whole dataset. Specialized literature offers several approaches to finding coresets, but such algorithms often disregard the application, or explicitly ask the user for the desired number of points. Starting from the consideration that finding coresets is an intuitively multi-objective problem, as minimizing the number of points goes against maintaining the original performance, in this paper we propose a multi-objective evolutionary approach to identifying coresets for classification. The proposed approach is tested on classical machine learning classification benchmarks, using 6 state-of-the-art classifiers, comparing against 7 algorithms for coreset discovery. Results show that not only the proposed approach is able to find coresets representing different compromises between compactness and performance, but that different coresets are identified for different classifiers, reinforcing the assumption that coresets might be closely linked to the specific application.

Background MicroRNAs (miRNAs) are noncoding RNA molecules heavily involved in human tumors, in which few of them circulating the human body. Finding a tumor-associated signature of miRNA, that is, the minimum miRNA entities to be measured for discriminating both different types of cancer and normal tissues, is of utmost importance. Feature selection techniques applied in machine learning can help however they often provide naive or biased results. Results An ensemble feature selection strategy for miRNA signatures is proposed. miRNAs are chosen based on consensus on feature relevance from high-accuracy classifiers of different typologies. This methodology aims to identify signatures that are considerably more robust and reliable when used in clinically relevant prediction tasks. Using the proposed method, a 100-miRNA signature is identified in a dataset of 8023 samples, extracted from TCGA. When running eight-state-of-the-art classifiers along with the 100-miRNA signature against the original 1046 features, it could be detected that global accuracy differs only by 1.4%. Importantly, this 100-miRNA signature is sufficient to distinguish between tumor and normal tissues. The approach is then compared against other feature selection methods, such as UFS, RFE, EN, LASSO, Genetic Algorithms, and EFS-CLA. The proposed approach provides better accuracy when tested on a 10-fold cross-validation with different classifiers and it is applied to several GEO datasets across different platforms with some classifiers showing more than 90% classification accuracy, which proves its cross-platform applicability. Conclusions The 100-miRNA signature is sufficiently stable to provide almost the same classification accuracy as the complete TCGA dataset, and it is further validated on several GEO datasets, across different types of cancer and platforms. Furthermore, a bibliographic analysis confirms that 77 out of the 100 miRNAs in the signature appear in lists of circulating miRNAs used in cancer studies, in stem-loop or mature-sequence form. The remaining 23 miRNAs offer potentially promising avenues for future research.

When a machine learning algorithm is able to obtain the same performance given a complete training set, and a small subset of samples from the same training set, the subset is termed coreset. As using a coreset improves training speed and allows human experts to gain a better understanding of the data, by reducing the number of samples to be examined, coreset discovery is an active line of research. Often in literature the problem of coreset discovery is framed as i. single-objective, attempting to find the candidate coreset that best represents the training set, and ii. independent from the machine learning algorithm used. In this work, an approach to evolutionary coreset discovery is presented. Building on preliminary results, the proposed approach uses a multi-objective evolutionary algorithm to find compromises between two conflicting objectives, i. minimizing the number of samples in a candidate coreset, and ii. maximizing the accuracy of a target classifier, trained with the coreset, on the whole original training set. Experimental results on popular classification benchmarks show that the proposed approach is able to identify candidate coresets with better accuracy and generality than state-of-the-art coreset discovery algorithms found in literature.

International audience; Background: Agri-food is one of the most important sectors of the industry in Europe and potentially a major contributor to the global warming. Sustainability issues in this context pose a huge challenge for several reasons: the variety of considered scales, the number of disciplines involved, the uncertainties, the out-of-equilibrium states, the complex quantitative and qualitative factors, the normative issues and the availability of data. Although important insight and breakthroughs have been attained in different scientific domains, an overarching and integrated analysis of these complex problems have yet to be realized.Scope and Approach: This context creates huge opportunities for research in interaction with mathematical programming, integrative models and decision-support tools. The paper propose a computational viewpoint including questions of holistic approach, multiscale reconstruction and optimization. Some directions are discussed.Key Findings and Conclusions: Several research questions based on a mathematical programming framework are emerging: how can such a framework manage uncertainty, cope with complex qualitative and quantitative information essential for social and environmental considerations, encompass diverse scales in space and time, cope with a multivariable dynamic environment and with scarcity of data. Moreover, how can it deal with different perspectives, types of models, research goals and data produced by conceptually disjoint scientific disciplines, ranging from physics and physiology to sociology and ethics? Building models is essential, but highly difficult; it will need a strong iterative interaction combining computational intensive methods, formal reasoning and the experts of the different fields. Some future research directions are proposed, involving all those dimensions: mathematical resilience, human-machine interactive learning and optimization techniques. (C) 2015 Elsevier Ltd. All rights reserved.

International audience; VALIS is an effective and robust classification algorithm with a focus on understandability. Its name stems from Vote-ALlocating Immune System, as it evolves a population of artificial antibodies that can bind to the input data, and performs classification through a voting process. In the beginning of the training, VALIS generates a set of random candidate antibodies; at each iteration, it selects the most useful ones to produce new candidates, while the least, are discarded; the process is iterated until a user-defined stopping condition. The paradigm allows the user to get a visual insight of the learning dynamics, helping to supervise the process, pinpoint problems, and tweak feature engineering. VALIS is tested against nine state-of-the-art classification algorithms on six popular benchmark problems; results demonstrate that it is competitive with well-established black-box techniques, and superior in specific corner cases.

International audience; Collectible card games have been among the most popular and profitable products of the entertainment industry since the early days of Magic: The GatheringTM in the nineties. Digital versions have also appeared, with HearthStone: Heroes of WarCraftTM being one of the most popular. In Hearthstone, every player can play as a hero, from a set of nine, and build his/her deck before the game from a big pool of available cards, including both neutral and hero-specific cards. This kind of games offers several challenges for researchers in artificial intelligence since they involve hidden information, unpredictable behaviour, and a large and rugged search space. Besides, an important part of player engagement in such games is a periodical input of new cards in the system, which mainly opens the door to new strategies for the players. Playtesting is the method used to check the new card sets for possible design flaws, and it is usually performed manually or via exhaustive search; in the case of Hearthstone, such test plays must take into account the chosen hero, with its specific kind of cards. In this paper, we present a novel idea to improve and accelerate the playtesting process, systematically exploring the space of possible decks using an Evolutionary Algorithm (EA). This EA creates HearthStone decks which are then played by an AI versus established human-designed decks. Since the space of possible combinations that are play-tested is huge, search through the space of possible decks has been shortened via a new heuristic mutation operator, which is based on the behaviour of human players modifying their decks. Results show the viability of our method for exploring the space of possible decks and automating the play-testing phase of game design. The resulting decks, that have been examined for balancedness by an expert player, outperform human-made ones when played by the AI; the introduction of the new heuristic operator helps to improve the obtained solutions, and basing the study on the whole set of heroes shows its validity through the whole range of decks.

One of the most relevant problems in social networks is influence maximization, that is the problem of finding the set of the most influential nodes in a network, for a given influence propagation model. As the problem is NP-hard, recent works have attempted to solve it by means of computational intelligence approaches, for instance Evolutionary Algorithms. However, most of these methods are of limited applicability for real-world large-scale networks, for two reasons: on the one hand, they require a large number of candidate solution evaluations to converge; on the other hand, each evaluation is computationally expensive in that it needs a considerable number of Monte Carlo simulations to obtain reliable values. In this work, we consider a possible solution to such limitations, by evaluating a surrogate-assisted Multi-Objective Evolutionary Algorithm that uses an approximate model of influence propagation (instead of Monte Carlo simulations) to find the minimum-sized set of most influential nodes. Experiments carried out on two social networks datasets suggest that approximate models should be carefully considered before using them in influence maximization approaches, as the errors induced by these models are in some cases too big to benefit the algorithmic performance.

The production system of freeze-dried lactic acid bacteria involves several processes, but its impact on bacteria resistance is still not well understood. This system can be defined as a complex one since it depends on multiple scales: the Genomic, the Cellular and the Population scale. The scarcity of data available for building models leads us to propose an approach that makes use of expert knowledge. In this paper we present a semiautomatic modelling tool, LIDEOGRAM and discuss how it contributes to insight formulation and rapid hypothesis testing. New results show that LIDEOGRAM is able to produce more robust modelling hypotheses when experts can interact and revisit the genomic data preprocessing.

International audience; Cancer diagnosis is currently undergoing a paradigm shift with the incorporation of molecular biomarkers as part of routine diagnostic panel. This breakthrough discovery directs researches to examine the role of microRNA in cancer, since its deregulation is often associated with almost all human tumors. Such differences frequently recur in tumor-specific microRNA signatures, which are helpful to diagnose tissue of origin and tumor subtypes. Nonetheless, the resulting classification problem is far from trivial, as there are hundreds of microRNA types, and tumors are non-linearly correlated to the presence of several overexpressions. In this paper, we propose to apply an evolutionary optimized convolutional neural network classifier to this complex task. The presented approach is compared against 21 state-of-the-art classifiers, on a real-world dataset featuring 8129 patients, for 29 different classes of tumors, using 1046 different biomarkers. As a result of the comparison, we also present a meta-analysis on the dataset, identifying the classes on which the collective performance of the considered classifiers is less effective, and thus possibly singling out types of tumors for which biomarker tests might be less reliable.

Since centuries, agriculture, food and biological systems are strongly linked to human expertise, albeit such knowledge has been capitalized and shared often at a local level, only. Since the beginning of the last century, swept away by productivism, modern agriculture and food production have put cumulated human knowledge aside. Facing new challenges like sustainability in a changing context, holistic approaches cannot be managed “manually” ab initio and there is a clear need for computing decision-support tools to tackle these new issues. Moreover, new approaches should be built centred on humans and for humans. The heart of our purpose is to shift the focus again on human and local expertise, guided by powerful computing interactive systems.

This article provides an overview of the 6 workshops held in conjunction with PPSN 2018 in Coimbra, Portugal. For each workshop, we list title, organizers, aim and scope as well as the accepted contributions.

International audience; Reducing the effort required by humans in countering malware is of utmost practical value. We describe a scalable, semi-supervised framework to dig into massive data sets of Android applications and identify new malware families. Until 2010, the industrial standard for the detection of malicious applications has been mainly based on signatures; as each tiny alteration in malware makes them ineffective, new signatures are frequently created - a task that requires a considerable amount of time and resources from skilled experts. The framework we propose is able to automatically cluster applications in families and suggest formal rules for identifying them with 100% recall and quite high precision. The families are used either to safely extend experts' knowledge on new samples or to reduce the number of applications requiring thorough analyses. We demonstrated the effectiveness and the scalability of the approach running experiments on a database of 1.5 million Android applications. In 2018, the framework has been successfully deployed on Koodous, a collaborative anti-malware platform.

International audience; This paper presents a novel model of protein hydrolysis and release of peptides by endoproteases. It requires the amino-acid sequence of the protein substrate to run, and makes use of simple Monte-Carlo in silico simulations to qualitatively and quantitatively predict the peptides that are likely to be produced during the course of the proteolytic reaction. In the present study, the model is applied to the case of pepsin, the gastric protease. Unlike pancreatic proteases, pepsin has a low substrate specificity and therefore displays a stochastic behavior that is particularly challenging to model and predict. Two versions of the model are studied and compared with peptidomic data obtained during pepsin hydrolysis of bovine lactoferrin. The first version of the model takes into account cleavage probabilities according to the amino acids in position P1-P1' only, whereas the second version also accounts for the influence of neighbor amino acids (P4, P3, P2, P2', P3', P4') and peptide terminal ends. The second version of the model was able to reproduce many real-world features of the reported behavior of pepsin, such as the peptide size distribution, or the quantity of free amino-acids. More remarkably, 50% of the experimentally monitored peptides (44/87) lay within the 120 most abundant simulated peptides. The presented methodology has the advantage of being applicable not only to different proteins, but to different enzymes as well, as long as cleavage frequency data are available.

In this paper the application of machine learning techniques for the development of constitutive material models is being investigated. A flow stress model, for strain rates ranging from 10 −4 to 1012 (quasi-static to highly dynamic), and temperatures ranging from room temperature to over 1000 K, is obtained by beginning directly with experimental stress–strain data for Copper. An incrementally objective and fully implicit time integration scheme is employed to integrate the hypo-elastic constitutive model, which is then implemented into a finite element code for evaluation. Accuracy and performance of the flow stress models derived from symbolic regression are assessed by comparison to Taylor anvil impact data. The results obtained with the free-form constitutive material model are compared to well-established strength models such as the Preston–Tonks–Wallace (PTW) model and the Mechanical Threshold Stress (MTS) model. Preliminary results show candidate free-form models comparing well with data in regions of stress–strain space with sufficient experimental data, pointing to a potential means for both rapid prototyping in future model development, as well as the use of machine learning in capturing more data as a guide for more advanced model development.

[EN] Diversity of food systems and their interaction with the environment has become a research topic for many years. Scientists use various models to explain environmental issues of food systems. This paper gives an overview of main streams in analyzing this topic. A literature review was performed by analyzing published scientific papers on environmental impacts in the food chain. The selection criteria were focused on different environmental approaches applied in the food chain and on the perspectives of future research. This review shows that on the one side there are generic environmental models developed by environmental scientists and as such applied on food. On the other side, there are models developed by food scientists in order to analyze food-environmental interactions. The environmental research in food industry can be categorized as product, process or system oriented. This study confirmed that the focus of product based approach is mainly performed through life-cycle assessments. The process based approach focuses on food processes such as heat transfer, cleaning and sanitation and various approaches in food waste management. Environmental systems in the food chain were the least investigated stream analyzing levels of environmental practices in place. Future research perspectives are the emerging challenges related to environmental impacts of novel food processing technologies, innovative food packaging and changes in diets and food consumption in connection with climate and environmental changes., The authors would like to acknowledge networking support by the COST Action CA15118 (FoodMC).

Inspiring metaphors play an important role in the beginning of an investigation, but are less important in a mature research field as the real phenomena involved are understood. Nowadays, in evolutionary computation, biological analogies should be taken into consideration only if they deliver significant advantages.

The analysis of worst-case behavior in wireless sensor networks is an extremely difficult task, due to the complex interactions that characterize the dynamics of these systems. In this paper, we present a new methodology for analyzing the performance of routing protocols used in such networks. The approach exploits a stochastic optimization technique, specifically an evolutionary algorithm, to generate a large, yet tractable, set of critical network topologies; such topologies are then used to infer general considerations on the behaviors under analysis. As a case study, we focused on the energy consumption of two well-known ad hoc routing protocols for sensor networks: the multi-hop link quality indicator and the collection tree protocol. The evolutionary algorithm started from a set of randomly generated topologies and iteratively enhanced them, maximizing a measure of “how interesting” such topologies are with respect to the analysis. In the second step, starting from the gathered evidence, we were able to define concrete, protocol-independent topological metrics which correlate well with protocols’ poor performances. Finally, we discovered a causal relation between the presence of cycles in a disconnected network, and abnormal network traffic. Such creative processes were made possible by the availability of a set of meaningful topology examples. Both the proposed methodology and the specific results presented here – that is, the new topological metrics and the causal explanation – can be fruitfully reused in different contexts, even beyond wireless sensor networks.

One of the most notable features of collectible card games is deckbuilding, that is, defining a personalized deck before the real game. Deckbuilding is a challenge that involves a big and rugged search space, with different and unpredictable behaviour after simple card changes and even hidden information. In this paper, we explore the possibility of automated deckbuilding: a genetic algorithm is applied to the task, with the evaluation delegated to a game simulator that tests every potential deck against a varied and representative range of human-made decks. In these preliminary experiments, the approach has proven able to create quite effective decks, a promising result that proves that, even in this challenging environment, evolutionary algorithms can find good solutions.

Genetic Improvement is an evolutionary-based technique. Despite its relatively recent introduction, several successful applications have been already reported in the scientific literature: it has been demonstrated able to modify the code complex programs without modifying their intended behavior; to increase performance with regards to speed, energy consumption or memory use. Some results suggest that it could be also used to correct bugs, restoring the software’s intended functionalities. Given the novelty of the technique, however, instances of Genetic Improvement so far rely upon ad-hoc, language-specific implementations. In this paper, we propose a general framework based on the software engineering’s idea of mutation testing coupled with Genetic Programming, that can be easily adapted to different programming languages and objective. In a preliminary evaluation, the framework efficiently optimizes the code of the md5 hash function in C, Java, and Python.

International audience; PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended without prior requirements. All PPSN attendees are cordially invited to take this opportunity to learn about ongoing research activities in our field!

International audience; The iterated prisoner’s dilemma is a famous model of cooperation and conflict in game theory. Its origin can be traced back to the Cold War, and countless strategies for playing it have been proposed so far, either designed by hand or automatically generated by computers. In the 2000s, scholars started focusing on adaptive players, that is, able to classify their opponent’s behavior and adopt an effective counter-strategy. The player presented in this paper, pushes such idea even further: it builds a model of the current adversary from scratch, without relying on any pre-defined archetypes, and tweaks it as the game develops using an evolutionary algorithm; at the same time, it exploits the model to lead the game into the most favorable continuation. Models are compact non-deterministic finite state machines; they are extremely efficient in predicting opponents’ replies, without being completely correct by necessity. Experimental results show that such player is able to win several one-toone games against strong opponents taken from the literature, and that it consistently prevails in round-robin tournaments of different sizes.

This short paper contains an extended list of references to diversity preservation methodologies, classified following the taxonomy presented in a previous publication. The list has been updated according to the contributions sent to the workshop "Measuring and Promoting Diversity in Evolutionary Computation", held during the conference GECCO 2016.

Chapitre 11; The use of anti-virus software has become something of an act of faith. A recent study showed that more than 80 % of all personal computers have anti-virus software installed. However, the protection mechanisms in place are far less effective than users would expect. Malware analysis is a classical example of cat-and-mouse game: as new anti-virus techniques are developed, malware authors respond with new ones to thwart analysis. Every day, anti-virus companies analyze thousands of malware that has been collected through honeypots, hence they restrict the research to only already existing viruses. This article describes a novel method for malware obfuscation based an evolutionary opcode generator and a special ad-hoc packer. The results can be used by the security industry to test the ability of their system to react to malware mutations.

Chapitre 5; Several machine learning paradigms have been applied to financial forecasting, attempting to predict the market’s behavior, with the final objective of profiting from trading shares. While anticipating the performance of such a complex system is far from trivial, this issue becomes even harder when the investors do not have large amounts of money available. In this paper, we present an evolutionary portfolio optimizer for the management of small budgets. The expected returns are modeled resorting to Multi-layer Perceptrons, trained on past market data, and the portfolio composition is chosen by approximating the solution to a multi-objective constrained problem. An investment simulator is then used to measure the portfolio performance. The proposed approach is tested on real-world data from Milan stock exchange, exploiting information from January 2000 to June 2010 to train the framework, and data from July 2010 to August 2011 to validate it. The presented tool is finally proven able to obtain a more than satisfying profit for the considered time frame.