Pub Date : 2011-06-05DOI: 10.1109/CEC.2011.5949694
Udit Halder, Swagatam Das, Dipankar Maity, A. Abraham, P. Dasgupta
In this paper we propose a Self Adaptive Cluster based and Weed Inspired Differential Evolution algorithm (SACWIDE), the total population is divided into several clusters based on the positions of the individuals and the cluster number is dynamically changed by the suitable learning strategy during evolution. Here we incorporate a modified version of the Invasive Weed Optimization (IWO) algorithm as a local search technique. The algorithm strategically determines whether a particular cluster will perform Differential Evolution (DE) or the IWO algorithm (modified). The number of clusters in a particular iteration is set by the algorithm itself self-adaptively. The performance of SACWIDE is reported on the set of 22 benchmark problems of CEC-2011.
{"title":"Self adaptive cluster based and weed inspired differential evolution algorithm for real world optimization","authors":"Udit Halder, Swagatam Das, Dipankar Maity, A. Abraham, P. Dasgupta","doi":"10.1109/CEC.2011.5949694","DOIUrl":"https://doi.org/10.1109/CEC.2011.5949694","url":null,"abstract":"In this paper we propose a Self Adaptive Cluster based and Weed Inspired Differential Evolution algorithm (SACWIDE), the total population is divided into several clusters based on the positions of the individuals and the cluster number is dynamically changed by the suitable learning strategy during evolution. Here we incorporate a modified version of the Invasive Weed Optimization (IWO) algorithm as a local search technique. The algorithm strategically determines whether a particular cluster will perform Differential Evolution (DE) or the IWO algorithm (modified). The number of clusters in a particular iteration is set by the algorithm itself self-adaptively. The performance of SACWIDE is reported on the set of 22 benchmark problems of CEC-2011.","PeriodicalId":293652,"journal":{"name":"2011 IEEE Congress of Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115729303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-06-05DOI: 10.1109/CEC.2011.5949809
M. Awad, K. D. Jong
Segmentation of satellite images is an important step for the success of the object detection and recognition in image processing. Segmentation is the process of dividing the image into disjoint homogeneous regions. There are many segmentation methods and approaches, the most popular are clustering methods and approaches such as Fuzzy C-Means (FCM) and K-means. The success of clustering methods depends strongly on the selection of the initial spectral signatures. Normally, this is done either manually or randomly, in either case the outcome is unpredictable. In this paper an unsupervised method based on Multi-Objective Genetic Algorithm (MOGA) for the selection of spectral signature from satellite images is described. The new method works by maximizing the number of the selected pixels (minimize over-segmentation) and by minimizing the difference between these pixels and their spectral signature (maximize homogeneity). Experimental results are conducted using a high resolution SPOT V satellite image, the collected spectral signatures, and the K-means clustering algorithm. The verification of the segmentation results is based on a very high resolution satellite image of type QuickBird. The spectral signatures provided to K-means by MOGA increased the speed of clustering to approximately 4 times the speed of the random based selection of signatures. At the same time MOGA improved the accuracy of the results of clustering using K-means to more than 10 %.
{"title":"Optimization of spectral signatures selection using multi-objective genetic algorithms","authors":"M. Awad, K. D. Jong","doi":"10.1109/CEC.2011.5949809","DOIUrl":"https://doi.org/10.1109/CEC.2011.5949809","url":null,"abstract":"Segmentation of satellite images is an important step for the success of the object detection and recognition in image processing. Segmentation is the process of dividing the image into disjoint homogeneous regions. There are many segmentation methods and approaches, the most popular are clustering methods and approaches such as Fuzzy C-Means (FCM) and K-means. The success of clustering methods depends strongly on the selection of the initial spectral signatures. Normally, this is done either manually or randomly, in either case the outcome is unpredictable. In this paper an unsupervised method based on Multi-Objective Genetic Algorithm (MOGA) for the selection of spectral signature from satellite images is described. The new method works by maximizing the number of the selected pixels (minimize over-segmentation) and by minimizing the difference between these pixels and their spectral signature (maximize homogeneity). Experimental results are conducted using a high resolution SPOT V satellite image, the collected spectral signatures, and the K-means clustering algorithm. The verification of the segmentation results is based on a very high resolution satellite image of type QuickBird. The spectral signatures provided to K-means by MOGA increased the speed of clustering to approximately 4 times the speed of the random based selection of signatures. At the same time MOGA improved the accuracy of the results of clustering using K-means to more than 10 %.","PeriodicalId":293652,"journal":{"name":"2011 IEEE Congress of Evolutionary Computation (CEC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114451546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-06-05DOI: 10.1109/CEC.2011.5949637
B. A. Garro, Juan Humberto Sossa Azuela, R. Vázquez
Artificial bee colony (ABC) algorithm has been used in several optimization problems, including the optimization of synaptic weights from an Artificial Neural Network (ANN). However, this is not enough to generate a robust ANN. For that reason, some authors have proposed methodologies based on so-called metaheuristics that automatically allow designing an ANN, taking into account not only the optimization of the synaptic weights as well as the ANN's architecture, and the transfer function of each neuron. However, those methodologies do not generate a reduced design (synthesis) of the ANN. In this paper, we present an ABC based methodology, that maximizes its accuracy and minimizes the number of connections of an ANN by evolving at the same time the synaptic weights, the ANN's architecture and the transfer functions of each neuron. The methodology is tested with several pattern recognition problems.
{"title":"Artificial neural network synthesis by means of artificial bee colony (ABC) algorithm","authors":"B. A. Garro, Juan Humberto Sossa Azuela, R. Vázquez","doi":"10.1109/CEC.2011.5949637","DOIUrl":"https://doi.org/10.1109/CEC.2011.5949637","url":null,"abstract":"Artificial bee colony (ABC) algorithm has been used in several optimization problems, including the optimization of synaptic weights from an Artificial Neural Network (ANN). However, this is not enough to generate a robust ANN. For that reason, some authors have proposed methodologies based on so-called metaheuristics that automatically allow designing an ANN, taking into account not only the optimization of the synaptic weights as well as the ANN's architecture, and the transfer function of each neuron. However, those methodologies do not generate a reduced design (synthesis) of the ANN. In this paper, we present an ABC based methodology, that maximizes its accuracy and minimizes the number of connections of an ANN by evolving at the same time the synaptic weights, the ANN's architecture and the transfer functions of each neuron. The methodology is tested with several pattern recognition problems.","PeriodicalId":293652,"journal":{"name":"2011 IEEE Congress of Evolutionary Computation (CEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114387674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-06-05DOI: 10.1109/CEC.2011.5949701
M. Wahib, Asim Munawar, M. Munetomo, K. Akama
Led by General Purpose computing over Graphical Processing Units (GPGPUs), the parallel computing area is witnessing a rapid change in dominant parallel systems. A major hurdle in this switch is the Single Instruction Multiple Thread (SIMT) architecture of GPUs which is usually not suitable for the design of legacy parallel algorithms. Genetic Algorithms (GAs) is no exception for that. GAs are commonly parallelized due to the high demanding computational needs. Given the performance of GPGPUs, the need to best exploit them to maximize computing efficiency for parallel GAs is demandingly growing. The goal of this paper is to shed light on the challenges parallel GAs designers/programmers will likely face while trying to achieve this, and to provide some practical advice on how to maximize GPGPU exploitation as a result. To that end, this paper provides a study on adapting legacy parallel GAs on GPGPU systems. The paper exposes the design challenges of nVidia's GPU architecture to the parallel GAs community by: discussing features of GPU, reviewing design issues in GPU relevant to parallel GAs, the design and introduction of new techniques to achieve an efficient implementation for parallel GAs and observing the effect of the pivotal points that both capitalize on the strengths of GPU and limit the deficiencies/overheads of GPUs. The paper demonstrates the performance of designed-for-GPGPU parallel GAs representing the entire spectrum of legacy parallel model of GAs over nVidia Tesla C1060 workstation showing a significant improvement in performance after optimizing and tuning the algorithms for GPU.
在图形处理单元(gpgpu)上的通用计算的引领下，并行计算领域正在见证主导并行系统的快速变化。这种切换的主要障碍是gpu的单指令多线程(SIMT)架构，该架构通常不适合传统并行算法的设计。遗传算法(GAs)也不例外。由于计算需求高，GAs通常是并行化的。考虑到gpgpu的性能，利用它们来最大化并行GAs的计算效率的需求正在不断增长。本文的目标是阐明并行GAs设计者/程序员在尝试实现这一目标时可能面临的挑战，并就如何最大限度地利用GPGPU提供一些实用的建议。为此，本文提供了在GPGPU系统上适配遗留并行GAs的研究。本文通过讨论GPU的特性，回顾GPU与并行GAs相关的设计问题，设计和引入新技术以实现并行GAs的有效实现，以及观察关键点的效果，这些关键点既利用了GPU的优势，又限制了GPU的不足/开销，从而向并行GAs社区揭示了nVidia GPU架构的设计挑战。本文在nVidia Tesla C1060工作站上展示了为gpgpu设计的并行GAs的性能，这些GAs代表了所有遗留的并行GAs模型，在优化和调整GPU算法后，性能有了显着的提高。
{"title":"Optimization of parallel Genetic Algorithms for nVidia GPUs","authors":"M. Wahib, Asim Munawar, M. Munetomo, K. Akama","doi":"10.1109/CEC.2011.5949701","DOIUrl":"https://doi.org/10.1109/CEC.2011.5949701","url":null,"abstract":"Led by General Purpose computing over Graphical Processing Units (GPGPUs), the parallel computing area is witnessing a rapid change in dominant parallel systems. A major hurdle in this switch is the Single Instruction Multiple Thread (SIMT) architecture of GPUs which is usually not suitable for the design of legacy parallel algorithms. Genetic Algorithms (GAs) is no exception for that. GAs are commonly parallelized due to the high demanding computational needs. Given the performance of GPGPUs, the need to best exploit them to maximize computing efficiency for parallel GAs is demandingly growing. The goal of this paper is to shed light on the challenges parallel GAs designers/programmers will likely face while trying to achieve this, and to provide some practical advice on how to maximize GPGPU exploitation as a result. To that end, this paper provides a study on adapting legacy parallel GAs on GPGPU systems. The paper exposes the design challenges of nVidia's GPU architecture to the parallel GAs community by: discussing features of GPU, reviewing design issues in GPU relevant to parallel GAs, the design and introduction of new techniques to achieve an efficient implementation for parallel GAs and observing the effect of the pivotal points that both capitalize on the strengths of GPU and limit the deficiencies/overheads of GPUs. The paper demonstrates the performance of designed-for-GPGPU parallel GAs representing the entire spectrum of legacy parallel model of GAs over nVidia Tesla C1060 workstation showing a significant improvement in performance after optimizing and tuning the algorithms for GPU.","PeriodicalId":293652,"journal":{"name":"2011 IEEE Congress of Evolutionary Computation (CEC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115067777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-06-05DOI: 10.1109/CEC.2011.5949593
Mouadh Yagoubi, L. Thobois, Marc Schoenauer
Master-slave parallelization of Evolutionary Algorithms (EAs) is straightforward, by distributing all fitness computations to slaves. The benefits of asynchronous steady-state approaches are well-known when facing a possible heterogeneity among the evaluation costs in term of runtime, be they due to heterogeneous hardware or non-linear numerical simulations. However, when this heterogeneity depends on some characteristics of the individuals being evaluated, the search might be biased, and some regions of the search space poorly explored. Motivated by a real-world case study of multi-objective optimization problem — the optimization of the combustion in a Diesel Engine — the consequences of different components of heterogeneity in the evaluation costs on the convergence of two Evolutionary Multi-objective Optimization Algorithms are investigated on artificially-heterogeneous benchmark problems. In some cases, better spread of the population on the Pareto front seem to result from the interplay between the heterogeneity at hand and the evolutionary search.
{"title":"Asynchronous Evolutionary Multi-Objective Algorithms with heterogeneous evaluation costs","authors":"Mouadh Yagoubi, L. Thobois, Marc Schoenauer","doi":"10.1109/CEC.2011.5949593","DOIUrl":"https://doi.org/10.1109/CEC.2011.5949593","url":null,"abstract":"Master-slave parallelization of Evolutionary Algorithms (EAs) is straightforward, by distributing all fitness computations to slaves. The benefits of asynchronous steady-state approaches are well-known when facing a possible heterogeneity among the evaluation costs in term of runtime, be they due to heterogeneous hardware or non-linear numerical simulations. However, when this heterogeneity depends on some characteristics of the individuals being evaluated, the search might be biased, and some regions of the search space poorly explored. Motivated by a real-world case study of multi-objective optimization problem — the optimization of the combustion in a Diesel Engine — the consequences of different components of heterogeneity in the evaluation costs on the convergence of two Evolutionary Multi-objective Optimization Algorithms are investigated on artificially-heterogeneous benchmark problems. In some cases, better spread of the population on the Pareto front seem to result from the interplay between the heterogeneity at hand and the evolutionary search.","PeriodicalId":293652,"journal":{"name":"2011 IEEE Congress of Evolutionary Computation (CEC)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117191153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-06-05DOI: 10.1109/CEC.2011.5949917
L. Dumas, Mohammed El Rhabi, G. Rochefort
This paper deals with a joint nonuniform illumination estimation and blind deconvolution for barcode signals by using evolutionary algorithms. Indeed, such optimization problems are highly non convex and a robust method is needed in case of noisy and/or blurred signals and nonuniform illumination. Here, we present the construction of a genetic algorithm combining discrete and continuous optimization which is successfully applied to decode real images with very strong noise and blur.
{"title":"An evolutionary approach for blind deconvolution of barcode images with nonuniform illumination","authors":"L. Dumas, Mohammed El Rhabi, G. Rochefort","doi":"10.1109/CEC.2011.5949917","DOIUrl":"https://doi.org/10.1109/CEC.2011.5949917","url":null,"abstract":"This paper deals with a joint nonuniform illumination estimation and blind deconvolution for barcode signals by using evolutionary algorithms. Indeed, such optimization problems are highly non convex and a robust method is needed in case of noisy and/or blurred signals and nonuniform illumination. Here, we present the construction of a genetic algorithm combining discrete and continuous optimization which is successfully applied to decode real images with very strong noise and blur.","PeriodicalId":293652,"journal":{"name":"2011 IEEE Congress of Evolutionary Computation (CEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116802263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-06-05DOI: 10.1109/CEC.2011.5949959
Aniesha Alford, Khary Popplewell, G. Dozier, Kelvin S. Bryant, John C. Kelly, Joshua Adams, Tamirat T. Abegaz, Joseph Shelton, K. Ricanek, D. Woodard
In this paper, we compare the performance of a Steady-State Genetic Algorithm (SSGA) and an Estimation of Distribution Algorithm (EDA) for multi-biometric feature selection and weighting. Our results show that when fusing face and periocular modalities, SSGA-based feature weighting (GEFeWSSGA) produces higher average recognition accuracies, while EDA-based feature selection (GEFeSEDA) performs better at reducing the number of features needed for recognition.
{"title":"A comparison of GEC-based feature selection and weighting for multimodal biometric recognition","authors":"Aniesha Alford, Khary Popplewell, G. Dozier, Kelvin S. Bryant, John C. Kelly, Joshua Adams, Tamirat T. Abegaz, Joseph Shelton, K. Ricanek, D. Woodard","doi":"10.1109/CEC.2011.5949959","DOIUrl":"https://doi.org/10.1109/CEC.2011.5949959","url":null,"abstract":"In this paper, we compare the performance of a Steady-State Genetic Algorithm (SSGA) and an Estimation of Distribution Algorithm (EDA) for multi-biometric feature selection and weighting. Our results show that when fusing face and periocular modalities, SSGA-based feature weighting (GEFeWSSGA) produces higher average recognition accuracies, while EDA-based feature selection (GEFeSEDA) performs better at reducing the number of features needed for recognition.","PeriodicalId":293652,"journal":{"name":"2011 IEEE Congress of Evolutionary Computation (CEC)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117190577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-06-05DOI: 10.1109/CEC.2011.5949836
Yu-Hsuan Huang, Chuan-Kang Ting
The multi-vehicle selective pickup and delivery problem (MVSPDP) is a class of vehicle routing problem. The MVSPDP aims to minimize the total distance traveled by a fleet of vehicles to collect and supply commodities, subject to vehicle capacity and travel distance. This problem relaxes the constraint that the vehicles have to visit all customers. In the MVSPDP, vehicles only need to collect sufficient commodities from some selected pickup nodes for all delivery nodes. To resolve the problem, this study develops a genetic algorithm with path relinking (GAPR). A repair operator is presented for the GAPR to handle the constraints. Experimental results on fourteen benchmarks validate the effectiveness of the proposed GAPR for the MVSPDP.
{"title":"Genetic algorithm with path relinking for the multi-vehicle selective pickup and delivery problem","authors":"Yu-Hsuan Huang, Chuan-Kang Ting","doi":"10.1109/CEC.2011.5949836","DOIUrl":"https://doi.org/10.1109/CEC.2011.5949836","url":null,"abstract":"The multi-vehicle selective pickup and delivery problem (MVSPDP) is a class of vehicle routing problem. The MVSPDP aims to minimize the total distance traveled by a fleet of vehicles to collect and supply commodities, subject to vehicle capacity and travel distance. This problem relaxes the constraint that the vehicles have to visit all customers. In the MVSPDP, vehicles only need to collect sufficient commodities from some selected pickup nodes for all delivery nodes. To resolve the problem, this study develops a genetic algorithm with path relinking (GAPR). A repair operator is presented for the GAPR to handle the constraints. Experimental results on fourteen benchmarks validate the effectiveness of the proposed GAPR for the MVSPDP.","PeriodicalId":293652,"journal":{"name":"2011 IEEE Congress of Evolutionary Computation (CEC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117278312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-06-05DOI: 10.1109/CEC.2011.5949906
J. Grobler, A. Engelbrecht, G. Kendall, V. Yadavalli
Algorithm selection is an important consideration in multi-method global optimization. This paper investigates the use of various algorithm selection strategies derived from well known evolutionary selection mechanisms. Selection strategy performance is evaluated on a diverse set of floating point benchmark problems and meaningful conclusions are drawn with regard to the impact of selective pressure on algorithm selection in a multi-method environment.
{"title":"Investigating the impact of alternative evolutionary selection strategies on multi-method global optimization","authors":"J. Grobler, A. Engelbrecht, G. Kendall, V. Yadavalli","doi":"10.1109/CEC.2011.5949906","DOIUrl":"https://doi.org/10.1109/CEC.2011.5949906","url":null,"abstract":"Algorithm selection is an important consideration in multi-method global optimization. This paper investigates the use of various algorithm selection strategies derived from well known evolutionary selection mechanisms. Selection strategy performance is evaluated on a diverse set of floating point benchmark problems and meaningful conclusions are drawn with regard to the impact of selective pressure on algorithm selection in a multi-method environment.","PeriodicalId":293652,"journal":{"name":"2011 IEEE Congress of Evolutionary Computation (CEC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124516108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-06-05DOI: 10.1109/CEC.2011.5949595
Xianneng Li, Bing Li, S. Mabu, K. Hirasawa
This paper proposed a novel EDA, where a directed graph network is used to represent its chromosome. In the proposed algorithm, a probabilistic model is constructed from the promising individuals of the current generation using reinforcement learning, and used to produce the new population. The node connection probability is studied to develop the probabilistic model, therefore pairwise interactions can be demonstrated to identify and recombine building blocks in the proposed algorithm. The proposed algorithm is applied to a problem of agent control, i.e., autonomous robot control. The experimental results show the superiority of the proposed algorithm comparing with the conventional algorithms.
{"title":"A novel estimation of distribution algorithm using graph-based chromosome representation and reinforcement learning","authors":"Xianneng Li, Bing Li, S. Mabu, K. Hirasawa","doi":"10.1109/CEC.2011.5949595","DOIUrl":"https://doi.org/10.1109/CEC.2011.5949595","url":null,"abstract":"This paper proposed a novel EDA, where a directed graph network is used to represent its chromosome. In the proposed algorithm, a probabilistic model is constructed from the promising individuals of the current generation using reinforcement learning, and used to produce the new population. The node connection probability is studied to develop the probabilistic model, therefore pairwise interactions can be demonstrated to identify and recombine building blocks in the proposed algorithm. The proposed algorithm is applied to a problem of agent control, i.e., autonomous robot control. The experimental results show the superiority of the proposed algorithm comparing with the conventional algorithms.","PeriodicalId":293652,"journal":{"name":"2011 IEEE Congress of Evolutionary Computation (CEC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124624491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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