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A scalable and customizable processor array for implementing cellular genetic algorithms

NEUROCOMPUTING 2016年第PartB期175卷 899-910页

作者： Letras, Martin Morales-Reyes, Alicia Cumplido, Rene Inst Nacl Astrofis Opt & Electr Dept Comp Sci Puebla 72840 Mexico

Architectures design for genetic algorithms (GAs) has proved its effectiveness to tackle hard real time constrained problems that require an optimization mechanism in one of their phases. Most of these approaches are problem dependent and cannot be easily adapted to other problems. Moreover, GAs based architectures preserve the algorithmic structure of a panmictic population in a sequential GA and therefore they are similar to a software implementation. Recently, combination of GAs, both sequential and parallel and reconfigurable devices such as FPGAs have been merged to create GAs based parallel hardware architectures. This study proposes a novel hardware architectural framework that implements a fine grained or cellular GAs while maintaining toroidal connection among individuals within the population. Achieving massive parallelism is limited by available resources;therefore, the proposed architectural design implements a segmentation strategy that partitions the entire decentralized population while maintaining original algorithmic interaction among solutions. The proposed architecture aims at preserving fine grained GAs algorithmic structure while improving resources usage. It also allows flexibility in terms of population and solutions representation size and the evaluation module containing the objective function is interchangeable. (C) 2015 Elsevier B.V. All rights reserved.

关键词： cellular genetic algorithms Hardware Architecture FPGA

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Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization

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SOFT COMPUTING 2011年第9期15卷 1749-1767页

作者： Ishibuchi, Hisao Sakane, Yuji Tsukamoto, Noritaka Nojima, Yusuke Osaka Prefecture Univ Grad Sch Engn Osaka Japan

In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection for mating, and the other is for local competition such as the fight for water and sunlight among neighboring plants. The aim of this paper is to show several implementations of cellular algorithms with two neighborhood structures for single-objective and multi-objective optimization problems. Since local selection has already been utilized in cellular algorithms in the literature, the main issue of this paper is how to implement the concept of local competition. We show three ideas about its utilization: Local elitism, local ranking, and local replacement. Local elitism and local ranking are used for single-objective optimization to increase the diversity of solutions. On the other hand, local replacement is used for multi-objective optimization to improve the convergence of solutions to the Pareto frontier. The main characteristic feature of our approach is that the two neighborhood structures can be specified independently of each other. Thus, we can separately examine the effect of each neighborhood structure on the behavior of cellular algorithms.

关键词： cellular genetic algorithms Neighborhood structures Structured demes Single-objective optimization Multi-objective optimization

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An Empirical Analysis on Dimensionality in cellular genetic algorithms 15

An Empirical Analysis on Dimensionality in Cellular Genetic ...

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17th genetic and Evolutionary Computation Conference (GECCO)

作者： Morales-Reyes, Alicia Jair Escalante, Hugo Letras, Martin Cumplido, Rene Inst Nacl Astrofis Opt & Electr Puebla Mexico

ISBN: (纸本)9781450334723

cellular or fine grained genetic algorithms (GAs) are a massively parallel algorithmic approach to GAs. Decentralizing their population allows alternative ways to explore and to exploit the solutions landscape. Individuals interact locally through nearby neighbours while the entire population is globally exploring the search space throughout a predefined population's topology. Having a decentralized population requires the definition of other algorithmic configuration parameters;such as shape and number of individuals within the local neighbourhood, population's topology shape and dimension, local instead of global selection criteria, among others. In this article, attention is paid to the population's topology dimension in cGAs. Several benchmark problems are assessed for 1, 2, and 3 dimensions while combining a local selection criterion that significantly affect overall selective pressure. On the other hand, currently available high performance processing platforms such as Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) offer massively parallel fabrics. Therefore, having a strong empirical base to understand structural properties in cellular GAs would allow to combine physical properties of these platforms when designing hardware architectures to tackle difficult optimization problems where timing constraints are mandatory.

关键词： cellular genetic algorithms Topology's Dimension Selective Pressure

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Advanced models of cellular genetic algorithms evaluated on SAT 05

Advanced models of cellular genetic algorithms evaluated on ...

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genetic and Evolutionary Computation Conference

作者： Alba, Enrique Alfonso, Hugo Dorronsoro, Bernabe Univ Malaga Dept Language & Comp Sci E-29071 Malaga Spain

ISBN: (纸本)1595930108

cellular genetic algorithms (cGAs) are mainly characterized by their spatially decentralized population, in which individuals can only interact with their neighbors. In this work, we study the behavior of a large number of different cGAs when solving the well-known 3-SAT problem. These cellular algorithms differ in the policy of individuals update and the population shape, since these two features affect the balance between exploration and exploitation of the algorithm. We study in this work both synchronous and asynchronous cGAs, having static and dynamically adaptive shapes for the population. Our main conclusion is that the proposed adaptive cGAs outperform other more traditional genetic algorithms for a well known benchmark of 3-SAT.

关键词： algorithms performance adaptation cellular genetic algorithms synchronicity

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Exploring Multi-objective cellular genetic algorithms in Community Detection Problems 21st

Exploring Multi-objective Cellular Genetic Algorithms in Com...

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21st International Conference on Intelligent Data Engineering and Automated Learning

作者： Pedemonte, Martin Panizo-LLedot, Angel Bello-Orgaz, Gema Camacho, David Univ Republica InCo Montevideo Uruguay Univ Politecn Madrid Dept Sistemas Informat Madrid Spain

ISBN: (纸本)9783030623647;9783030623654

Interest in network analysis has not stopped increasing over the last decade. The Community Detection Problem (CDP) has been a hot topic in network analysis, so many different approaches have been proposed. Among them, optimization methods have proven to be highly effective for this task. Traditionally, the CDP has been tackled as a single-objective optimization problem. Nevertheless, this trend has started to change, and new methods have appeared following multi-objective approaches. genetic algorithms have been applied to the CDP with relative success, especially NSGA-II. However, cellular genetic algorithms (cGAs) have yet received little attention. In cGAs, the population is structured in small overlapping neighborhoods producing a slow spread of high-quality solutions. The main contribution of this paper is understanding if the smooth diffusion scheme of MoCell (a multi-objective cGA) can provide any benefit over current multi-objective GAs for the CDP. To verify the effectiveness of MoCell, an evaluation was conducted on 21 synthetically generated networks and two real-world ones. The experiments show that MoCell is able to outperform NSGA-II, especially in large networks scenarios.

关键词： Static community detection problem Network analysis Multi-objective Evolutionary algorithms cellular genetic algorithms

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A novel partition strategy for efficient implementation of 3D cellular genetic algorithms

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MICROPROCESSORS AND MICROSYSTEMS 2024年 104卷

作者： Letras, Martin Morales-Reyes, Alicia Cumplido, Rene Martinez-Penaloza, Maria-Guadalupe Feregrino-Uribe, Claudia Inst Nacl Astrofis Opt & Elect INAOE Puebla 72840 Mexico Friedrich Alexander Univ Erlangen Nurnberg D-91058 Bayern Germany

Solving optimization problems while fulfilling real-time constraints requires high algorithmic and processing performance. cellular genetic algorithms (cGAs) have been competitive at difficult single objective combina-torial and continuous domain problems. Moreover, it has been demonstrated that structural properties in cGAs, such as population topology dimension, local neighborhood configuration and ad-hoc selection mechanisms, allow not only further algorithmic improvement but also, these characteristics can be combined at hardware level for acceleration. In this article, a novel partition strategy to exploit 3D cGAs population dynamics on a 2D processing array using Field Programmable Gate Arrays (FPGAs) as the target processing platform is presented. The proposed architecture fits as an optimization module within an embedded system where real-time constraints must be fulfilled. Therefore, it is important to find an optimal trade-off between hardware resources usage and searching time. Overall results demonstrate that the proposed architecture can run up to 90 MHz when tackling continuous benchmark functions. Moreover, speed-up of up to three and two orders of magnitude are achieved in comparison to a single CPU and a parallel GPU respectively.

关键词： Optimization methods cellular genetic algorithms Field Programmable Gate Arrays Parallel hardware architectures

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A stop-and-start adaptive cellular genetic algorithm for mobility management of GSM-LTE cellular network users

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 106卷 290-304页

作者： Dahi, Zakaria Abdelmoiz Alba, Enrique Draa, Amer Constantine 2 Univ Dept Fundamental Comp Sci & Applicat Constantine Algeria Dept Lenguajes & Ciencias Computac Malaga Spain

The optimisation of the user tracking process is one of the most challenging tasks in today's advanced cellular networks. In this paper, we propose a new low-complexity adaptive cellular genetic algorithm to solve this problem. The proposed approach uses a torus-like structured population of candidate solutions and regulates interactions inside it by using a bi-dimensional neighbourhood. It also automatically adapts the algorithm's parameters and regenerates the algorithm's population using two algorithmically light operators. In order to draw reliable conclusions and perform an encompassing assessment, extensive experiments have been conducted on 25 differently-sized realistic networks. The proposed approach has been compared against 26 state-of-the-art algorithms previously designed to solve the mobility management problem, and a thorough statistical analysis of results has been performed. The obtained results have shown that our proposal is more efficient and algorithmically less complex than most of the state-of-the-art solvers. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： cellular networks cellular genetic algorithms Adaptation

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Inference of gene regulatory networks with multi-objective cellular genetic algorithm

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2019年第0期80卷 409-418页

作者： Garcia-Nieto, Jose Nebro, Antonio J. Aldana-Montes, Jose F. Univ Malaga ETSI Infomtat Dept Lenguajes & Ciencias Comp Campus Teatinos E-29071 Malaga Spain Univ Malaga ETSI Infomtat Inst Invest Biomed Malaga IBIMA Campus Teatinos E-29071 Malaga Spain

Reverse engineering of biochemical networks remains an important open challenge in computational systems biology. The goal of model inference is to, based on time-series gene expression data, obtain the sparse topological structure and parameters that quantitatively understand and reproduce the dynamics of biological systems. In this paper, we propose a multi-objective approach for the inference of S-System structures for Gene Regulatory Networks (GRNs) based on Pareto dominance and Pareto optimality theoretical concepts instead of the conventional single-objective evaluation of Mean Squared Error (MSE). Our motivation is that, using a multi-objective formulation for the GRN, it is possible to optimize the sparse topology of a given GRN as well as the kinetic order and rate constant parameters in a decoupled S-System, yet avoiding the use of additional penalty weights. A flexible and robust Multi-Objective cellular Evolutionary Algorithm is adapted to perform the tasks of parameter learning and network topology inference for the proposed approach. The resulting software, called MONET, is evaluated on real-based academic and synthetic time-series of gene expression taken from the DREAM3 challenge and the IRMA in vivo datasets. The ability to reproduce biological behavior and robustness to noise is assessed and compared. The results obtained are competitive and indicate that the proposed approach offers advantages over previously used methods. In addition, MONET is able to provide experts with a set of trade-off solutions involving GRNs with different typologies and MSEs.

关键词： Multi-objective optimization cellular genetic algorithms Gene regulatory networks DREAM challenge

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A Niche cellular genetic Algorithm Based on Evolution Rule

A Niche Cellular Genetic Algorithm Based on Evolution Rule

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3rd International Conference on Manufacturing Science and Engineering (ICMSE 2012)

作者： Zhu, Dalin Zhan, Teng Tang, Rui Li, Huifang Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance China Three Gorges University China College of Foreign Languages China Three Gorges University China

ISBN: (纸本)9783037853733

Since the individuals' reproduction and existing probability can be greatly improved by introducing evolution rule into cellular automata algorithm, this paper presents a new niche cellular genetic algorithm by involving cellular automata evolution rule and introducing niche technique. The results of simulation experiments on some well-known complex benchmarking problems reveal that this algorithm outperforms the basic cellular genetic algorithms (cGAs) and cGAs with evolution rule in terms of diversity, convergence, global optimization and stability.

关键词： cellular automata cellular genetic algorithms evolution rule niche technique

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On the improvement of the real time recurrent learning algorithm for recurrent neural networks

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NEUROCOMPUTING 1999年第1-3期24卷 13-36页

作者： Mak, MW Ku, KW Lu, YL Hong Kong Polytech Univ Dept Elect Engn Hong Kong Peoples R China

This paper reviews different approaches to improving the real time recurrent learning (RTRL) algorithm and attempts to group them into common frameworks. The characteristics of sub-grouping strategy, mode exchange RTRL, and cellular genetic algorithms are discussed. The relationships between these algorithms are highlighted and their time complexities and convergence capability are compared. The learning algorithms are applied to train recurrent neural networks in an attempt to solve a long-term dependency problem, to model the Henon map, and to predict the chaotic intensity pulsations of an NH, laser. The results show that the original RTRL algorithm achieves the lowest error among the gradient-based algorithms, but it requires the longest training time;whereas the sub-grouping strategy uses the shortest training time but its convergence capability is the poorest. The results also demonstrate that the cellular genetic algorithm is an alternative means of training recurrent neural networks when the gradient-based methods fail to find an acceptable solution. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： recurrent neural networks real-time recurrent learning cellular genetic algorithms chaotic series

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