Pub Date : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862080
Veronica Chan, Christine W. Chan
This paper discusses the development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems, the algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. Rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The developed algorithm was applied to nineteen datasets. The preliminary results showed that the algorithm gives satisfactory results on sixteen of the nineteen tested datasets and the results demonstrate high fidelity to the originally trained neural network models. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that there are more factors affecting the fidelity of the algorithm apart from the precision of the approximation of each individual node of the given ANN model. Nevertheless, the algorithm shows promising potential for application in engineering problems.
{"title":"Development and application of an algorithm for extracting multiple linear regression equations from artificial neural networks for nonlinear regression problems","authors":"Veronica Chan, Christine W. Chan","doi":"10.1109/ICCI-CC.2016.7862080","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862080","url":null,"abstract":"This paper discusses the development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems, the algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. Rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The developed algorithm was applied to nineteen datasets. The preliminary results showed that the algorithm gives satisfactory results on sixteen of the nineteen tested datasets and the results demonstrate high fidelity to the originally trained neural network models. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that there are more factors affecting the fidelity of the algorithm apart from the precision of the approximation of each individual node of the given ANN model. Nevertheless, the algorithm shows promising potential for application in engineering problems.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130912489","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 : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862091
P. Guo, A. Evans, P. Bhattacharya
There are challenges for image cancer nuclei segmentation in clinical decision support systems for brain tumor diagnosis. In this study, we propose a method for segmentation of cancer nuclei when such conflicts of cancer nuclei involve ‘omics’ indicative of brain tumors pathologically. To constrain the problem space in the region of color information (i.e. cancer nuclei), we begin by converting the images into the V component of HSV (Hue, Saturation, Value) using the level-set segmentation (VLS) in the training stage, follow by applying the sparsity representation (SR) in the test stage. Via the SR, the proposed VLS-SR would exhibits an improved capability of searching recursively for the optimal threshold level-set in the working subsets of the SR for image cancer nuclei segmentation.
{"title":"Segmentation of nuclei in digital pathology images","authors":"P. Guo, A. Evans, P. Bhattacharya","doi":"10.1109/ICCI-CC.2016.7862091","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862091","url":null,"abstract":"There are challenges for image cancer nuclei segmentation in clinical decision support systems for brain tumor diagnosis. In this study, we propose a method for segmentation of cancer nuclei when such conflicts of cancer nuclei involve ‘omics’ indicative of brain tumors pathologically. To constrain the problem space in the region of color information (i.e. cancer nuclei), we begin by converting the images into the V component of HSV (Hue, Saturation, Value) using the level-set segmentation (VLS) in the training stage, follow by applying the sparsity representation (SR) in the test stage. Via the SR, the proposed VLS-SR would exhibits an improved capability of searching recursively for the optimal threshold level-set in the working subsets of the SR for image cancer nuclei segmentation.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133267920","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 : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862082
L. M. Zhang
A common artificial neural network (ANN) uses the same activation function for all hidden and output neurons. Therefore, it has an optimization limitation for complex big data analysis due to its single mathematical functionality. In addition, an ANN with a complicated activation function uses a very long training time and consumes a lot of energy. To address these issues, this paper presents a new energy-efficient “Multifunctional Neural Network” (MNN) that uses a variety of different activation functions to effectively improve performance and significantly reduce energy consumption. A generic training algorithm is designed to optimize the weights, biases, and function selections for improving performance while still achieving relatively fast computational time and reducing energy usage. A novel general learning algorithm is developed to train the new energy-efficient MNN. For performance analysis, a new “Genetic Deep Multifunctional Neural Network” (GDMNN) uses genetic algorithms to optimize the weights and biases, and selects the set of best-performing energy-efficient activation functions for all neurons. The results from sufficient simulations indicate that this optimized GDMNN can perform better than other GDMNNs in terms of achieving high performance (prediction accuracy), low energy consumption, and fast training time. Future works include (1) developing more effective energy-efficient learning algorithms for the MNN for data mining application problems, and (2) using parallel cloud computing methods to significantly speed up training the MNN.
常见的人工神经网络(ANN)对所有隐藏神经元和输出神经元使用相同的激活函数。因此，由于数学功能单一，对复杂的大数据分析存在优化限制。此外，激活函数复杂的人工神经网络训练时间长，能量消耗大。为了解决这些问题，本文提出了一种新的节能“多功能神经网络”(MNN)，该网络使用多种不同的激活函数来有效提高性能并显着降低能耗。设计了一种通用的训练算法来优化权重、偏置和函数选择，以提高性能，同时仍然实现相对较快的计算时间和减少能量使用。提出了一种新的通用学习算法来训练新型节能MNN。在性能分析方面，一种新的“遗传深度多功能神经网络”(Genetic Deep Multifunctional Neural Network, GDMNN)利用遗传算法对权重和偏置进行优化，并为所有神经元选择性能最佳的节能激活函数集。大量的仿真结果表明，优化后的GDMNN在实现高性能(预测精度)、低能耗和快速训练时间方面优于其他GDMNN。未来的工作包括(1)为MNN开发更有效节能的学习算法，用于数据挖掘应用问题，以及(2)使用并行云计算方法显着加快MNN的训练速度。
{"title":"A new multifunctional neural network with high performance and low energy consumption","authors":"L. M. Zhang","doi":"10.1109/ICCI-CC.2016.7862082","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862082","url":null,"abstract":"A common artificial neural network (ANN) uses the same activation function for all hidden and output neurons. Therefore, it has an optimization limitation for complex big data analysis due to its single mathematical functionality. In addition, an ANN with a complicated activation function uses a very long training time and consumes a lot of energy. To address these issues, this paper presents a new energy-efficient “Multifunctional Neural Network” (MNN) that uses a variety of different activation functions to effectively improve performance and significantly reduce energy consumption. A generic training algorithm is designed to optimize the weights, biases, and function selections for improving performance while still achieving relatively fast computational time and reducing energy usage. A novel general learning algorithm is developed to train the new energy-efficient MNN. For performance analysis, a new “Genetic Deep Multifunctional Neural Network” (GDMNN) uses genetic algorithms to optimize the weights and biases, and selects the set of best-performing energy-efficient activation functions for all neurons. The results from sufficient simulations indicate that this optimized GDMNN can perform better than other GDMNNs in terms of achieving high performance (prediction accuracy), low energy consumption, and fast training time. Future works include (1) developing more effective energy-efficient learning algorithms for the MNN for data mining application problems, and (2) using parallel cloud computing methods to significantly speed up training the MNN.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127359529","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 : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862097
F. Beaufays
In the last ten years, speech recognition has evolved from a science fiction dream to a widespread input method for mobile devices. In this talk, I will describe how speech recognition works, the problems we have solved and the challenges that remain. I will touch upon some of Google's main efforts in language and pronunciation modeling, and describe how the adoption of neural networks for acoustic modeling marked the beginning of a technology revolution in the field, with approaches such as Long Short Term Memory models and Connectionist Temporal Classification. I will also share my learnings on how Machine Learning and Human Knowledge can be harmoniously combined to build state-of-the-art technology that helps and delights users across the world.
{"title":"Learnings and innovations in speech recognition","authors":"F. Beaufays","doi":"10.1109/ICCI-CC.2016.7862097","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862097","url":null,"abstract":"In the last ten years, speech recognition has evolved from a science fiction dream to a widespread input method for mobile devices. In this talk, I will describe how speech recognition works, the problems we have solved and the challenges that remain. I will touch upon some of Google's main efforts in language and pronunciation modeling, and describe how the adoption of neural networks for acoustic modeling marked the beginning of a technology revolution in the field, with approaches such as Long Short Term Memory models and Connectionist Temporal Classification. I will also share my learnings on how Machine Learning and Human Knowledge can be harmoniously combined to build state-of-the-art technology that helps and delights users across the world.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114201858","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 : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862041
É. Grégoire, Jean-Marie Lagniez, Du Zhang
We claim that computing forms of consensus among several agents about their solutions to past problems can play a useful pre-treatment role in case-based reasoning. Intuitively, we define a consensus as a subset of the plain accumulation of all the agents' individual past discovered solutions such that every agent can agree on all the information in this subset. A consensus can be expected to form a more reliable basis for further re-use or generalization than the knowledge from which it is extracted. We define various forms of logical consensus in this context: the focus is on computational issues about the automated extraction of consensuses in an extended Boolean logic setting.
{"title":"Logical consensuses for case-based reasoning and for mathematical engineering of AI","authors":"É. Grégoire, Jean-Marie Lagniez, Du Zhang","doi":"10.1109/ICCI-CC.2016.7862041","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862041","url":null,"abstract":"We claim that computing forms of consensus among several agents about their solutions to past problems can play a useful pre-treatment role in case-based reasoning. Intuitively, we define a consensus as a subset of the plain accumulation of all the agents' individual past discovered solutions such that every agent can agree on all the information in this subset. A consensus can be expected to form a more reliable basis for further re-use or generalization than the knowledge from which it is extracted. We define various forms of logical consensus in this context: the focus is on computational issues about the automated extraction of consensuses in an extended Boolean logic setting.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132380340","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 : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862066
Giacomo Briochi, M. Colombetti, M. D. Hina, Assia Soukane, A. Ramdane-Cherif
In this work, given the context of the driver, of the vehicle and of the environment, our objective is to correctly recognize the traffic situation and provide the driver with the corresponding assistance by providing notification or alert about the situation or the infraction that was committed, or acting directly on the vehicle. To do so, we need to consider the signal processing related to these context parameters. We built knowledge representation using ontology, built rules related to the fusion of context parameters and the deduction corresponding to the traffic situation using Semantic Web Rule Language. We built fission component that deals with traffic situation and the corresponding action directed towards the driver or the vehicle. Ontology is used to represent driving model and road environment. This work is our contribution in the ongoing research for the prevention of vehicular traffic accident.
{"title":"Techniques for cognition of driving context for safe driving application","authors":"Giacomo Briochi, M. Colombetti, M. D. Hina, Assia Soukane, A. Ramdane-Cherif","doi":"10.1109/ICCI-CC.2016.7862066","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862066","url":null,"abstract":"In this work, given the context of the driver, of the vehicle and of the environment, our objective is to correctly recognize the traffic situation and provide the driver with the corresponding assistance by providing notification or alert about the situation or the infraction that was committed, or acting directly on the vehicle. To do so, we need to consider the signal processing related to these context parameters. We built knowledge representation using ontology, built rules related to the fusion of context parameters and the deduction corresponding to the traffic situation using Semantic Web Rule Language. We built fission component that deals with traffic situation and the corresponding action directed towards the driver or the vehicle. Ontology is used to represent driving model and road environment. This work is our contribution in the ongoing research for the prevention of vehicular traffic accident.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123103117","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 : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862088
Michaël Guedj
The stable marriage problem is a well-known problem with many practical applications. Most algorithms to find stable marriages assume that the participants explicitly express a preference ordering. This can be problematic when the number of options is large or has a combinatorial structure. We show, by simply asking the actors (men and women) to fulfill a personal profile with items positioning in a tree-structured semantic network, that it is possible to solve the problem of stable marriages without asking the actors to explicitly operate a ranking over the members of the opposite sex.
{"title":"Ranking preferences deduction based on semantic similarity for the stable marriage problem","authors":"Michaël Guedj","doi":"10.1109/ICCI-CC.2016.7862088","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862088","url":null,"abstract":"The stable marriage problem is a well-known problem with many practical applications. Most algorithms to find stable marriages assume that the participants explicitly express a preference ordering. This can be problematic when the number of options is large or has a combinatorial structure. We show, by simply asking the actors (men and women) to fulfill a personal profile with items positioning in a tree-structured semantic network, that it is possible to solve the problem of stable marriages without asking the actors to explicitly operate a ranking over the members of the opposite sex.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126602731","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 : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862026
S. Izumi, Asato Edo, Toru Abe, T. Suganuma
In this paper, we propose a disaster-aware smart routing scheme for highly-available information storage systems. Our proposed scheme is based on the concept of Symbiotic Computing to recognize disaster status in Real Space, and provides appropriate routes form Digital Space dynamically. This realizes effective data transmission considering disaster situation and its time variation. We have designed architecture of our proposed scheme and conducted basic experimentation. In this paper, we extend its architecture based on the Symbiotic Computing and evaluate its effectiveness through complex network environments.
{"title":"Disaster-aware smart routing scheme based on symbiotic computing for highly-available information storage systems","authors":"S. Izumi, Asato Edo, Toru Abe, T. Suganuma","doi":"10.1109/ICCI-CC.2016.7862026","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862026","url":null,"abstract":"In this paper, we propose a disaster-aware smart routing scheme for highly-available information storage systems. Our proposed scheme is based on the concept of Symbiotic Computing to recognize disaster status in Real Space, and provides appropriate routes form Digital Space dynamically. This realizes effective data transmission considering disaster situation and its time variation. We have designed architecture of our proposed scheme and conducted basic experimentation. In this paper, we extend its architecture based on the Symbiotic Computing and evaluate its effectiveness through complex network environments.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116822045","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 : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862043
Mahsa Kiani, V. Bhavsar, H. Boley
In earlier work, Attributed Generalized Tree (AGT) structures, having vertex labels, edge labels, and edge weights have been introduced. AGTs can represent knowledge in domains containing rich semantic/pragmatic object-centered descriptions as well as complex relations between objects. Therefore, AGTs have applications in many domains such as health, business, and finance (e.g., insurance underwriting). In this paper, we introduce a function to quantify the simplicity of an arbitrary AGT. Our simplicity function takes into account branch, position, and weight factors; it maps the structure to a value in the interval [0,1]. The recursive simplicity algorithm performs a top-down traversal of the AGT and computes its simplicity bottom-up. Characteristic properties of the AGT simplicity measure are analyzed, and AGTs in a test dataset are ranked based on their simplicity values computed using our simplicity algorithm. The experimental analysis confirms our expectation that the simplicity value decreases with increasing the complexity of AGT structure.
{"title":"Semantic computing of simplicity in attributed generalized trees","authors":"Mahsa Kiani, V. Bhavsar, H. Boley","doi":"10.1109/ICCI-CC.2016.7862043","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862043","url":null,"abstract":"In earlier work, Attributed Generalized Tree (AGT) structures, having vertex labels, edge labels, and edge weights have been introduced. AGTs can represent knowledge in domains containing rich semantic/pragmatic object-centered descriptions as well as complex relations between objects. Therefore, AGTs have applications in many domains such as health, business, and finance (e.g., insurance underwriting). In this paper, we introduce a function to quantify the simplicity of an arbitrary AGT. Our simplicity function takes into account branch, position, and weight factors; it maps the structure to a value in the interval [0,1]. The recursive simplicity algorithm performs a top-down traversal of the AGT and computes its simplicity bottom-up. Characteristic properties of the AGT simplicity measure are analyzed, and AGTs in a test dataset are ranked based on their simplicity values computed using our simplicity algorithm. The experimental analysis confirms our expectation that the simplicity value decreases with increasing the complexity of AGT structure.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129875206","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 : 2016-08-01DOI: 10.1109/ICCI-CC.2016.7862030
Hieu V. Dang, W. Kinsner
Multiobjective memetic optimization algorithms (MMOAs) are recently applied to solve nonlinear optimization problems with conflicting objectives. An important issue in an MMOA is how to identify the relative best solutions to guide its adaptive processes. Pareto dominance has been used extensively to find the relative relations between solutions for the fitness assessment in multiobjective optimization based on evolutionary algorithms (MOEA). However, the approach based on the Pareto dominance criterion decreases its convergence speed when the number of objectives increases. In this paper, we propose an effective information-theoretic criterion based on the multiscale relative Rényi entropy to guide the adaptive selection, clustering, and local learning processes in our framework of adaptive multiobjective memetic optimization algorithms (AMMOA). The implementation of AMMOA is applied to several benchmark test problems with remarkable results.
{"title":"An information theoretic criterion for adaptive multiobjective memetic optimization","authors":"Hieu V. Dang, W. Kinsner","doi":"10.1109/ICCI-CC.2016.7862030","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2016.7862030","url":null,"abstract":"Multiobjective memetic optimization algorithms (MMOAs) are recently applied to solve nonlinear optimization problems with conflicting objectives. An important issue in an MMOA is how to identify the relative best solutions to guide its adaptive processes. Pareto dominance has been used extensively to find the relative relations between solutions for the fitness assessment in multiobjective optimization based on evolutionary algorithms (MOEA). However, the approach based on the Pareto dominance criterion decreases its convergence speed when the number of objectives increases. In this paper, we propose an effective information-theoretic criterion based on the multiscale relative Rényi entropy to guide the adaptive selection, clustering, and local learning processes in our framework of adaptive multiobjective memetic optimization algorithms (AMMOA). The implementation of AMMOA is applied to several benchmark test problems with remarkable results.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128608536","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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