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parallel alternatives for evolutionary multi-objective optimization in unsupervised feature selection

EXPERT SYSTEMS WITH APPLICATIONS 2015年第9期42卷 4239-4252页

作者： Kimovski, Dragi Ortega, Julio Ortiz, Andres Banos, Raul Univ Informat Sci & Technol Ohrid North Macedonia Univ Granada CITIC Dept Comp Architecture & Technol E-18071 Granada Spain Univ Malaga Dept Commun Engn E-29071 Malaga Spain Catholic Univ Murcia Dept Business Adm & Management Murcia Spain

Many machine learning and pattern recognition applications require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed. This way, feature selection has become an important issue on these researching areas. Nevertheless, as in past years the number of patterns and, more specifically, the number of features to be selected have grown very fast, parallel processing constitutes an important tool to reach efficient approaches that make possible to tackle complex problems within reasonable computing times. In this paper we propose parallel multi-objective optimization approaches to cope with high-dimensional feature selection problems. Several parallel multi-objective evolutionary alternatives are proposed, and experimentally evaluated by using some synthetic and BCI (Brain Computer Interface) benchmarks. The experimental results show that the cooperation of parallel evolving subpopulations provides improvements in the solution quality and computing time speedups depending on the parallel alternative and data profile. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Feature selection High-dimensional data Multi-objective clustering parallel evolutionary algorithms Speedup models Unsupervised classification

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parallel evolutionary approaches for game playing and verification using Intel Xeon Phi

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2019年 133卷 258-271页

作者： Rodriguez, Sebastian Parodi, Facundo Nesmachnow, Sergio Univ Republica Fac Ingn Montevideo Uruguay

Automatic generation of artificial players is an important subject for the videogames industry. Different strategies have been proposed to implement realistic and intelligent agents for gameplaying and verification. This article presents a parallel evolutionary approach for the automation of computer player generation for video games. A learning pipeline model is defined to study the generation problem for Nintendo Entertainment System games composed of three stages: objective inference, objective refinement and artificial intelligence generation. Two case studies based on the defined pipeline are presented: an evolutionary algorithm to learn how to play the game Pinball, offloading the evaluation of the fitness function to a Xeon Phi coprocessor, and a full pipeline implementation that uses neuroevolution to generate RNNs that can play different games successfully. Results show that the proposed pipeline can be applied for the automatic generation of artificial players for the studied games. (C) 2018 Elsevier Inc. All rights reserved.

关键词： parallel evolutionary algorithms Xeon Phi NES Neuroevolution

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PSFGA:: parallel processing and evolutionary computation for multiobjective optimisation

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parallel COMPUTING 2004年第5-6期30卷 721-739页

作者： Negro, FD Ortega, J Ros, E Mota, S Paechter, B Martín, JM Univ Huelva Dept Elect Engn & Comp Sci EPS Rabida Huelva 21047 Spain Univ Granada Dept Comp Technol & Comp Architecture ETSI Informat E-18071 Granada Spain Napier Univ Sch Comp Edinburgh EH10 5DT Midlothian Scotland

This paper deals with the study of the cooperation between parallel processing and evolutionary computation to obtain efficient procedures for solving multiobjective optimisation problems. We propose a new algorithm called PSFGA (parallel single front genetic algorithm), an elitist evolutionary algorithm for multiobjective problems with a clearing procedure that uses a grid in the objective space for diversity maintaining purposes. Thus, PSFGA is a parallel genetic algorithm with a structured population in the form of a set of islands. The performance analysis of PSFGA has been carried out in a cluster system and experimental results show that our parallel algorithm provides adequate results in both, the quality of the solutions found and the time to obtain them. It has been shown that its sequential version also outperforms other previously proposed sequential procedures for multiobjective optimisation in the cases studied. (C) 2004 Elsevier B.V. All rights reserved.

关键词： parallel evolutionary algorithms multiobjective optimisation cluster of computers

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P-RnaPredict -: A parallel evolutionary algorithm for RNA folding:: Effects of pseudorandom number quality

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2005年第3期4卷 219-227页

作者： Wiese, KC Hendriks, A Deschênes, A Ben Youssef, B Simon Fraser Univ Sch Comp Sci Surrey BC V3T 2W1 Canada Simon Fraser Univ Sch Interact Arts & Technol Surrey V3T 2W1 BC Canada

This paper presents a fully parallel version of RnaPredict, a genetic algorithm (GA) for RNA secondary structure prediction. The research presented here builds on previous work and examines the impact of three different pseudorandom number generators (PRNGs) on the GA's performance. The three generators tested are the C standard library PRNG RAND, a parallelized multiplicative congruential generator (MCG), and a parallelized Mersenne Twister (MT). A fully parallel version of RnaPredict using the Message Passing Interface (MPI) was implemented on a 128-node Beowulf cluster. The PRNG comparison tests were performed with known structures whose sequences are 118, 122, 468, 543, and 556 nucleotides in length. The effects of the PRNGs are investigated and the predicted structures are compared to known structures. Results indicate that P-RnaPredict demonstrated good prediction accuracy, particularly so for shorter sequences.

关键词： bioinformatics parallel evolutionary algorithms random number generators RNA secondary structure prediction

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SCHEDULING IN HETEROGENEOUS COMPUTING AND GRID ENVIRONMENTS USING A parallel CHC evolutionary ALGORITHM

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COMPUTATIONAL INTELLIGENCE 2012年第2期28卷 131-155页

作者： Nesmachnow, Sergio Alba, Enrique Cancela, Hector Univ Republica Montevideo Uruguay Univ Malaga E-29071 Malaga Spain

Scheduling is a capital problem when using distributed heterogeneous computing (HC) and grid environments to solve complex problems. The scheduling problem in heterogeneous environments is NP-hard, so a significant effort has been made to develop efficient methods for solving the problem. However, few works have faced realistic grid-sized problem instances. This work presents a parallel CHC (pCHC) evolutionary algorithm codified over MALLBA, a general-purpose library for combinatorial optimization, for solving the scheduling problem in HC and grid environments. Efficient numerical results are reported in the experimental analysis performed on both a standard benchmark and a set of large-sized problem instances specially designed in this work. The comparative study shows that pCHC is able to achieve high problem solving efficacy, significantly improving over traditional deterministic scheduling methods, while also showing a good scalability behavior when solving large problem instances.

关键词： grid heterogeneous computing parallel evolutionary algorithms scheduling

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A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling

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APPLIED SOFT COMPUTING 2012年第2期12卷 626-639页

作者： Nesmachnow, Sergio Cancela, Hector Alba, Enrique Univ Republica Montevideo Uruguay Univ Malaga E-29071 Malaga Spain

This work presents a novel parallel micro evolutionary algorithm for scheduling tasks in distributed heterogeneous computing and grid environments. The scheduling problem in heterogeneous environments is NP-hard, so a significant effort has been made in order to develop an efficient method to provide good schedules in reduced execution times. The parallel micro evolutionary algorithm is implemented using MALLBA, a general-purpose library for combinatorial optimization. Efficient numerical results are reported in the experimental analysis performed on both well-known problem instances and large instances that model medium-sized grid environments. The comparative study of traditional methods and evolutionary algorithms shows that the parallel micro evolutionary algorithm achieves a high problem solving efficacy, outperforming previous results already reported in the related literature, and also showing a good scalability behavior when facing high dimension problem instances. (C) 2011 Elsevier B. V. All rights reserved.

关键词： parallel evolutionary algorithms Scheduling Heterogeneous computing Grid

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High performance parallel evolutionary algorithm model based on MapReduce framework

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2013年第3期46卷 290-295页

作者： Du, Xin Ni, Youcong Yao, Zhiqiang Xiao, Ruliang Xie, Datong Fujian Normal Univ Fac Software Fuzhou 350108 Fujian Peoples R China Fujian Commercial Coll Dept Informat Management Engn Fuzhou 350012 Fujian Peoples R China

evolutionary algorithms (EAs) are increasingly being applied to large-scale problems. MapReduce is a powerful abstraction proposed by Google for making scalable and fault tolerant applications. However, how to design high performance parallel EA based on MapReduce (MR-PEA) is still an open problem. In this paper, a parallel evolutionary algorithm model based on MapReduce by improving traditional parallel evolutionary algorithms model is proposed. The MR-PEA model is fit for large populations and datasets, has the characteristic of high scalable and efficiency. In order to justify the effectiveness of the MR-PEA model, we proposed a parallel gene expression programming based on MapReduce (MR-GEP) used to solve symbolic regression.

关键词： evolutionary algorithms EAs parallel evolutionary algorithms MapReduce

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Multiobjective grid scheduling using a domain decomposition based parallel micro evolutionary algorithm

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2013年第1期4卷 70-84页

作者： Nesmachnow, Sergio Iturriaga, Santiago Univ Republica Fac Ingn Ctr Calculo Numer Comp CtrComp Sci Herrera Reissig 565 Montevideo Uruguay

This work studies the problem of scheduling independent tasks in heterogeneous computing grid systems. A new bi-objective formulation of the scheduling problem is introduced, which aims at minimising the makespan and weighted response ratio objectives. A novel parallel micro evolutionary algorithm is developed in order to efficiently solve the problem. By using a domain decomposition approach, the proposed method allows to efficiently deal with the multiobjective optimisation version of the scheduling problem. The new decomposition-based parallel micro evolutionary algorithm is implemented over MALLBA, a general-purpose library for combinatorial optimisation. The experimental analysis performed on both well-known and new large problem instances that model medium-sized grid environments demonstrate that the new parallel micro evolutionary algorithm achieves a high problem-solving efficacy and shows very good scalability behaviour when facing high-dimensional instances.

关键词： parallel evolutionary algorithms scheduling heterogeneous computing grid

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parallel Multi-Objective evolutionary Design of Approximate Circuits 15

Parallel Multi-Objective Evolutionary Design of Approximate ...

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17th Genetic and evolutionary Computation Conference (GECCO)

作者： Hrbacek, Radek Brno Univ Technol Fac Informat Technol CS-61090 Brno Czech Republic

ISBN: (纸本)9781450334723

evolutionary design of digital circuits has been well established in recent years. Besides correct functionality, the demands placed on current circuits include the area of the circuit and its power consumption. By relaxing the functionality requirement, one can obtain more efficient circuits in terms of the area or power consumption at the cost of an error introduced to the output of the circuit. As a result, a variety of trade-offs between error and efficiency can be found. In this paper, a multi-objective evolutionary algorithm for the design of approximate digital circuits is proposed. The scalability of the evolutionary design has been recently improved using parallel implementation of the fitness function and by employing spatially structured evolutionary algorithms. The proposed multi-objective approach uses Cartesian Genetic Programming for the circuit representation and a modified NSGA-II algorithm. Multiple isolated islands are evolving in parallel and the populations are periodically merged and new populations are distributed across the islands. The method is evaluated in the task of approximate arithmetical circuits design.

关键词： Cartesian Genetic Programming parallel evolutionary algorithms Multi-objective Optimization Cluster Combinational Circuit Design Approximate Circuits

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A parallel evolutionary Approach to Community Detection in Complex Networks 13

A Parallel Evolutionary Approach to Community Detection in C...

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13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Joldos, Marius Chira, Camelia Tech Univ Cluj Napoca Baritiu 26 Cluj Napoca 400027 Romania

ISBN: (纸本)9781538633687

The problem of community detection in complex networks is of high interest in many application domains including sociology, biology, mathematics and economy. Given a set of nodes and links between them, the aim of the problem is to find a grouping of nodes such that a strong community has dense intra-connections and sparse outside community links. In this paper, a coarse-grained evolutionary algorithm (EA) is developed to address this challenging problem. Several populations of potential solutions are evolved in parallel in an island model and periodically exchange certain individuals. Each population can be evolved by a different fitness function and several approaches to evaluate the community structure are considered in the current paper. Experiments are performed for real-world complex networks and results are analysed based on the normalized mutual information between the detected and the known community structure. Comparisons with the standard version of the EA based on different fitness functions are performed and the results confirm a good performance of the parallel EA in terms of solution quality and computational time.

关键词： complex networks community detection evolutionary algorithms parallel evolutionary algorithms

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