Sana Ben Hamida

Paris Nanterre University and Paris Dauphine University · SEGMI (NANTERRE) and LAMSADE (Dauphine)

Community detection in protein-protein interaction networks (PPIs) is an active area of research, and many studies have applied Genetic Algorithms (GAs) to this problem. This paper summarizes the different GA based approaches for community detection in PPIs and provides a taxonomy of these methods. Detailed comparative studies are then provided com...

Classifying imbalanced data is a big challenge for machine learning techniques, especially for medical data. To deal with this challenge, many solutions have been proposed. The most famous methods are based on the Synthetic Minority Over-sampling Technique (SMOTE), which creates new synthetic instances in the minority class. In this paper, we study...

In the era of petabyte, robust machine learning tools are needed to cope with the volume and high dimensionality of data to min. Evolutionary Algorithms (EA), such as Genetic Programming (GP), are powerful machine learning techniques with great potential to deal with big data challenges. To better exploit their capacities, additional manipulations...

Introduction: MEGDEL syndrome is a rare recessive disorder, with about 100 cases reported worldwide, which is defined by 3-methylglutaconic aciduria (MEG), deafness (D), encephalopathy (E), Leigh-like syndrome (L). When these manifestations were added to hepatopathy (H), the syndrome was labeled as MEGD(H)EL. Mutations in SERAC1 gene encoding a se...

Improving decisions by better mining the available data in an Information System is a common goal in many decision making environments. However, the complexity and the large size of the collected data in modern systems make this goal a challenge for mining methods. Evolutionary Data Mining Algorithms (EDMA), such as Genetic Programming (GP), are po...

Complexity, variety and large sizes of data bases make the Knowledge extraction a difficult task for supervised machine learning techniques. It is important to provide these techniques additional tools to improve their efficiency when dealing with such data. A promising strategy is to reduce the size of the training sample seen by the learner and t...

Community detection has become an important research direction for data mining in complex networks. It aims to identify topo-logical structures and discover patterns in complex networks, which presents an important problem of great significance. Prediction of communities from Protein-Protein Interaction (PPI) networks is important problem in system...

Improving decisions by better mining the available data in an Information System is a common goal in many decision making environments. However, the complexity and the large size of the collected data in modern systems make this goal a challenge for mining methods. Evolutionary Data Mining Algorithms (EDMA), such as Genetic Programming (GP), are po...

Complexity, variety and large sizes of data bases make the Knowledge extraction a difficult task for supervised machine learning techniques. It is important to provide these techniques additional tools to improve their efficiency when dealing with such data. A promising strategy is to reduce the size of the training sample seen by the learner and t...

Active learning is a machine learning paradigm allowing to decide which inputs to use for training. It is introduced to Genetic Programming (GP) essentially thanks to the dynamic data sampling, used to address some known issues such as the computational cost, the over-fitting problem and the imbalanced databases. The traditional dynamic sampling fo...

Community detection has become an important research direction for data mining in complex networks. It aims to identify topological structures and discover patterns in complex networks, which presents an important problem of great significance. In this paper, we are interested in the detection of communities in the Protein-Protein or Gene-gene Inte...

Genetic Algorithms (GA) have long been used for ordering optimization problems with some considerable efforts to improve their exploration and exploitation abilities. A great number of GA implementations have been proposed varying from GAs applying simple or advanced variation operators to hybrid GAs combined with different heuristics. In this work...

Scientific workflows are used to model scalable, portable, and reproducible big data analyses and scientific experiments with low development costs. To optimize their performances and ensure data resources efficiency, scientific workflows handling big volumes of data need to be executed on scalable distributed environments like the Cloud infrastruc...

Community detection aims to identify topological structures and discover patterns in complex networks. It presents an important problem of great significance in many fields. In this paper, we are interested in the detection of communities in biological networks. These networks represent protein-protein or gene-gene interactions which corresponds to...

The purpose of this paper is to improve the accuracy of dynamic hedging using implied volatilities generated by genetic programming. Using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes implied volatility is compared between static and dynamic training-subset selection methods. The performance o...

Volatility is a key variable in option pricing, trading and hedging strategies. The purpose of this paper is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training-subset selection methods. These methods manipulate the training data in order to improve the out of sample...

Genetic Programming (GP) is afflicted by an excessive computation time that is more exacerbated with data intensive problems. This issue has been addressed with different approaches such as sampling techniques or distributed implementations. In this paper, we focus on dynamic sampling algorithms that mostly give to GP learner a new sample each gene...

With the growing number of available databases having a very large number of records, existing knowledge discovery tools need to be adapted to this shift and new tools need to be created. Genetic Programming (GP) has been proven as an efficient algorithm in particular for classification problems. Notwithstanding, GP is impaired with its computing c...

The community detection in large networks is an important problem in many scientific fields ranging from Biology to Sociology and Computer Science. In this paper, we are interested in the detection of communities in the Protein-protein or Gene-gene Interaction (PPI) networks. These networks represent protein-protein or gene-gene interactions which...

We are interested in the detection of communities in biological networks. We focus more precisely on gene interaction networks. They represent protein-protein or gene-gene interactions. A community in such networks corresponds to a set of proteins or genes that collaborate at the same cellular function. Our goal is to identify such network or commu...

Extract knowledge and significant information from very large data sets is a main topic in Data Science, bringing the interest of researchers in machine learning field. Several machine learning techniques have proven e�ective to deal with massive data like Deep Neuronal Networks. Evolutionary algorithms are considered not well suitable for such pro...

Alain Pétrowski and Sana Ben‑Hamida: Evolutionary Algorithms John Wiley and Sons, Inc., Hoboken, New Jersey, USA, 2017, ISBN-13: 978-1848218048, ISBN-10: 1848218044 Keith Downing Published online: 9 June 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Petrowski and Ben-Hamid provide a veritable cookbook of evolutionary al...

Genetic programming (GP) is considered as the evolutionary technique having the widest range of application domains. It can be used to solve problems in at least three main fields: optimization, automatic programming and machine learning. This chapter summarizes the different GP implementations based on one of the three representations: tree-based...

This chapter presents the basic principles of evolutionary algorithms and a collection of widespread selection and variation operators. During the 1960s and 1970s, as soon as calculators of more credible capacity became accessible, many attempts to model the process of evolution were undertaken. Among those, three approaches emerged and progressed...

This chapter presents the basic concepts of the application of genetic and evolutionary algorithm (GEA) to combinatorial problems. Using GEAs for solving combinatorial problems needs at first to determine how to encode solutions and how to evolve solutions. EAs for combinatorial optimization use convenient encoding and variation operators that avoi...

Evolutionary algorithms are population algorithms able to provide a sample of non-dominated solutions in a single run when faced with multi-objective optimization problems. Two kinds of evolutionary approaches are widely considered in the literature: the methods using a Pareto ranking to evaluate the fitness function; and the aggregation methods th...

The first attempts to solve continuous optimization problems with genetic algorithms were based on binary coding of real numbers. This chapter presents three leading evolutionary approaches of today. These are the canonical covariance matrix adaptation evolution strategy (CMA-ES), differential evolution (DE) algorithms, and success-history based ad...

This chapter presents the constrained optimization, describes the different approaches for handling constraints in evolutionary computation and presents a survey of some of the methods for each approach. It also discusses the effectiveness of some of these approaches and provides the results of a comparison between three of them. The simplest way t...

Optimization problems from the industrial world must often respect a number of constraints. These are expressed as a set of relationships that the variables of the objective function must satisfy. These relationships are usually presented as equalities and inequalities that may be very hard to deal with.

The biological evolution generated extremely complex autonomous living beings which can solve extraordinarily difficult problems, such as the continuous adaptation to complex, uncertain environments and in perpetual transformation.

The amount of available data for data mining and knowledge discovery continue to grow very fast with the era of Big Data. Genetic Programming algorithms (GP), that are efficient machine learning techniques, are face up to a new challenge that is to deal with the mass of the provided data. Active Sampling, already used for Active Learning, might be...

Big data processing is the new challenge for analytical and machine learning techniques. Many efforts are needed to scale both classic and advanced methods to the the mass of the provided data. Evolutionary learning algorithms (EAL) are robust and effective methods in solving a wide variety of complex learning problems. This paper discusses how to...

in Springer series Advances in Intelligent Systems and Computing, vol. 529

Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter...

Evolutionary algorithms are bio-inspired algorithms based on Darwin's theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods. In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms. Chapter...

Volatility is a key variable in option pricing, trading, and hedging strategies. The purpose of this article is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training-subset selection methods. These methods manipulate the training data in order to improve the out-of-samp...

Quantization of a continuous-value signal into discrete form is a standard task in all analog/digital devices commonly used to solve numerical problems in finance. In this paper, we consider quantization of the Normal distribution. We suggest an hybrid technique based on the evolutionary optimization and the Stochastic Gradient for obtaining an...

Les métaheuristiques et leurs applications Les ingénieurs, les économistes, les décideurs se heurtent quotidiennement, quel que soit leur secteur d'activité, à des problèmes d'optimisation. Il peut s'agir de minimiser un coût de production, d'optimiser le parcours d'un véhicule ou le rendement d'un portefeuille boursier, de rationaliser l'utilisat...

Les métaheuristiques et leurs applications Les ingénieurs, les économistes, les décideurs se heurtent quotidiennement, quel que soit leur secteur d'activité, à des problèmes d'optimisation. Il peut s'agir de minimiser un coût de production, d'optimiser le parcours d'un véhicule ou le rendement d'un portefeuille boursier, de rationaliser l'utilisat...

The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility's forecasting. By using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is co...

Adaptivity has become a key issue in Evolutionary Algorithms, since early works in Evolution Strategies. The idea of letting the algorithm adjust its own parameters for free is indeed appealing. This paper proposes to use adaptive mechanisms at the population level for constrained optimization problems in three important steps of the evolutionary a...

We propose a new method based on evolutionary op- timization for obtaining an optimal Lp-quantizer of a multidimen- sional random variable. First, we remind briefly the main results about quantization. Then, we present the classical gradient-based approach (this approach is well detailed in (2) and (7) for p=2) used up to now to find a "local" opti...

We propose a probabilistic approach for estimating parameters of an option pricing model from a set of observed option prices. Our approach is based on a stochastic optimization algorithm which generates a random sample from the set of global minima of the in-sample pricing error and allows for the existence of multiple global minima. Starting from...

We propose a probabilistic approach for estimating parameters of an option pricing model from a set of observed option prices. Our approach is based on a stochastic optimization algorithm which generates a random sample from the set of global minima of the in-sample pricing error and allows for the existence of multiple global minima. Starting from...

We propose a probabilistic approach for estimating parameters of an option pricing model from a set of observed option prices. Our approach is based on a stochastic optimization algorithm which generates a random sample from the set of global minima of the in-sample pricing error and allows for the existence of multiple global minima. Starting from...

Several specific methods have been proposed for handling nonlinear constraints. These methods have to bring individuals in the feasible space, and help to explore and exploit efficiently the feasible domain. However, even if this domain is not sparse, this paper demonstrates that the exploration capacity of standard reproduction operators is not op...

ASCHEA is an adaptive algorithm for constrained optimization problem based on a population level adaptive penalty function to handle constraints, a constraint-driven mate selection for recombination, and a segregational selection that favors a given number of feasible individuals. In this paper, we present some new results obtained using ASCHEA aft...

Cette thèse est une étude théorique et expérimentale dans le domaine du calcul évolutionnaire. La première partie propose une introduction au domaine de l'évolution artificielle avec une synthèse des principales approches.La deuxième partie est une étude théorique consacrée à la prise en compte des contraintes dans l'optimisation évolutionnaire. So...

Several methods have been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems. The most widely used are those based on penalty function, thanks to their simplicity. In this paper, we propose a new adaptative penalty approach for solving constrained optimization problems, based on the amount of...

Several specific methods have been proposed for handling nonlinear constraints. These methods have to bring individuals in the feasible space, and help to explore and exploit efficiently the feasible domain. However, even if this domain is not sparse, this paper demonstrates that the exploration capacity of standard reproduction operators is not op...

The goal is to design the 2-dimensional profile of an optical lens in order to control focal-plane irradiance of some laser beam. The numerical simulation of the irradiance of the beam through the lens, including some technological constraints on the correlation radius of the phase of the lens, involves two FFT computations, whose computational cos...

Four of the currently recognized autosomal recessive limb-girdle muscular dystrophies (LGMD type 2C-F) are caused by mutations in the genes encoding components of the sarcoglycan complex. LGMD 2C, caused by mutations in gamma-sarcoglycan, is prevalent in northern Africa, especially in Tunisia, where this type of muscular dystrophy was originally de...

The LGMD2C linked to chromosome 13q and related to a 35 KDa dystrophin-associated glycoprotein deficiency, is very similar to Duchenne muscular dystrophy with an autosomal recessive inheritance. It is characterized by a variability of the age of onset, the severity of the evolution and the severity of myopathic changes at the muscle biopsy. This va...