Sébastien Cahon

ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics

In this paper, we present the ParadisEO white-box object-oriented framework dedicated to the reusable design of parallel and distributed metaheuristics (PDM). ParadisEO provides a broad range of features including evolutionary algorithms (EA), local searches (LS), the most common parallel and distributed models and hybridization mechanisms, etc. This high content and utility encourages its use at European level. ParadisEO is based on a clear conceptual separation of the solution methods from the problems they are intended to solve. This separation confers to the user a maximum code and design reuse. Furthermore, the fine-grained nature of the classes provided by the framework allow a higher flexibility compared to other frameworks. ParadisEO is of the rare frameworks that provide the most common parallel and distributed models. Their implementation is portable on distributed-memory machines as well as on shared-memory multiprocessors, as it uses standard libraries such as MPI, PVM and PThreads. The models can be exploited in a transparent way, one has just to instantiate their associated provided classes. Their experimentation on the radio network design real-world application demonstrate their efficiency.

Grid computing for parallel bioinspired algorithms

This paper focuses on solving large size combinatorial optimization problems using a Grid-enabled framework called ParadisEO-CMW (Parallel and Distributed EO on top on Condor and the Master Worker Framework). The latter is an extension of ParadisEO, an open source framework originally intended to the design and deployment of parallel hybrid meta-heuristics on dedicated clusters and networks of workstations. Relying on the Condor-MW framework, it enables the execution of these applications on volatile heterogeneous computational pools of resources. The motivations, architecture and main features will be discussed. The framework has been experimented on a real-world problem: feature selection in near-infrared spectroscopic data mining. It has been solved by deploying a multi-level parallel model of evolutionary algorithms. Experimentations have been carried out on more than 100 PCs originally intended for education. The obtained results are convincing, both in terms of flexibility and easiness at implementation, and in terms of efficiency, quality and robustness of the provided solutions at run time.

Building with paradisEO reusable parallel and distributed evolutionary algorithms

Numerous parallel and distributed evolutionary algorithms (PDEAs) and their implementations have been proposed and are available on the Web. A robust approach to make easier their code and design reuse is the framework approach. In this paper, we present some existing frameworks for PDEAs and their development requirements, and propose a new C++ open source framework, named Parallel and distributed Evolving Objects (ParadisEO). ParadisEO is basically devoted to the reusable and flexible design of parallel and distributed metaheuristics, but we focus here only on PDEAs. Compared to other related frameworks, ParadisEO allows more reuse flexibility, and provides more implemented parallel and distributed models. Furthermore, these models can be exploited by the user in a transparent way, and deployed as well on shared memory multi-processors as on distributed memory machines. The architecture has been experimented on two real-world applications: the radio network design and the spectroscopic data mining. The experimental results demonstrate the efficiency and robustness of the different models.

An enabling framework for parallel optimization on the computational grid

In this paper, we present ParadisEO-CMW, an extension of the open source ParadisEO framework, originally intended to the design and deployment of parallel hybrid meta heuristics on dedicated clusters of SMPs. Coupled with the Condor-MW library, it enables the execution of such parallel applications on volatile heterogeneous computational resources. The motivations, architecture and main features will be discussed. The framework has been tested by tackling a real-world NP-hard problem: feature selection in near-infrared spectroscopic data mining. It has been resolved by deploying a multi-level parallel model of evolutionary algorithms. Experimentations have been carried out on more than one hundred PCs originally intended for education. The obtained results are convincing, both in terms of flexibility and easiness at implementation, and in terms of efficiency and quality of provided solutions at execution.

ParadisEO: a framework for parallel and distributed biologically inspired heuristics

In this paper we present ParadisEO, an open source framework for flexible parallel and distributed design of hybrid metaheuristics. Flexibility means that the parameters such as data representation and variation operators can be evolved. It is inherited from the EO object-oriented library for evolutionary computation. ParadisEO provides different parallel and/or distributed models and allows a transparent multi-threaded implementation. Moreover, it supplies different natural hybridization mechanisms mainly for metaheuristics including evolutionary algorithms and local search methods. The framework is experimented here in the spectroscopic data mining field. The flexibility property allowed an easy and straightforward development of a geneticalgorithm-based attribute selection for models discovery in NIR spectroscopic data. Experiments on a cluster of SMPs (IBM SP3) show that a good speed-up is achieved by using the provided parallel distributed models and multi-threading. Furthermore, the hybridization of the GA with the efficient PLS method allows to discover high-quality models. Indeed, their accuracy and understandability are improved respectively by 37% and 88%.

Parallel GA-based wrapper feature selection for spectroscopic data mining

Mining predictive models in dense databases is CPU time consuming and I/O intensive. In this paper, we propose a taxonomy of existing techniques allowing to achieve high performance. We propose a hybrid approach allowing to exploit four of them: feature selection, GA-based exploration space reduction, parallelism and concurrency. The approach is experimented on a near-infrared (NIR) spectroscopic application. It consists of predicting the concentration of a given component in a given product from its absorbances to NIR radiations. Statistical methods, like PLS, are well-suited and efficient for such data mining task. The experimental results show that preceding those methods with a feature selection allows to withdraw a significant number of irrelevant features and at the same time to enhance significantly the accuracy of the discovered predictive model. It is also shown that for the considered task the GA-based approach allows to build more accurate models than neural networks. Moreover, the parallel multithreaded implementation of the approach allows a linear speed-up.

GGM: Efficient Navigation and Mining in Distributed Genomedical Data

The integration of genomics and patient related data is considered as one of the most promising investigation topic in health care research. Started in 2004, the Grid for Geno Medicine (GGM) project aims at providing a comprehensive grid software infrastructure designed to allow biologists to mine and analyze relationships between medical, genetic, and genomic data stored in distributed datawarehouses. The proposed layered service oriented architecture offers a number of independent but compliant services that can be deployed in a grid environment. This paper presents these services insisting on their integration into a common software platform, the use case that is carried out. It also presents the current state of the developments and of the performance evaluations.

ParaDisEO-Based Design of Parallel and Distributed Evolutionary Algorithms

ParaDisEO is a framework dedicated to the design of parallel and distributed metaheuristics including local search methods and evolutionary algorithms. This paper focuses on the latter aspect. We present the three parallel and distributed models implemented in ParaDisEO and show how these can be exploited in a user-friendly, flexible and transparent way. These models can be deployed on distributed memory machines as well as on shared memory multi-processors, taking advantage of the shared memory in the latter case. In addition, we illustrate the instantiation of the models through two applications demonstrating the efficiency and robustness of the framework.

Using EAs for Error Prediction in Near Infrared Spectroscopy

This paper presents an evolutionary approach to estimate the sugar concentration inside compound bodies based on spectroscopy measurements. New European regulation will shortly forbid the use of established chemical methods based on mercury to estimate the sugar concentration in sugar beet. Spectroscopy with a powerful regression technique called PLS (Partial Least Squares) may be used instead. We show that an evolutionary approach for selecting relevant wavelengths before applying PLS can lower the error and decrease the computation time. It is submitted that the results support the argument for replacing the PLS scheme with a GP technique.