Pub Date : 2015-12-01DOI: 10.1109/ICCIC.2015.7435804
P. Bohra, Dr Hemant Palivela
Support Vector Machines (SVM's) are supervised learning algorithms which can be used for analyzing patterns and classifying data. This supervised algorithm is applicable for binary class as well as multiclass classification. The core idea is to build a hyperplane which can easily separate the training examples. For binary class, SVM constructs a hyper-plane which can easily separate d-dimensional training examples perfectly into 2-classes. but sometimes, the training examples are not linearly separable. Thus, for non-linear training examples, SVM introduced Kernel functions which transforms the data into high dimensional space where the data can be separated linearly. For minimizing the test error and for improving classification accuracy, kernels functions are used. This paper explains applications of kernels in support vector machine and provide information about the properties of these kernels and situations in which they can be used.
{"title":"Understanding and formulation of various kernel techniques for suport vector machines","authors":"P. Bohra, Dr Hemant Palivela","doi":"10.1109/ICCIC.2015.7435804","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435804","url":null,"abstract":"Support Vector Machines (SVM's) are supervised learning algorithms which can be used for analyzing patterns and classifying data. This supervised algorithm is applicable for binary class as well as multiclass classification. The core idea is to build a hyperplane which can easily separate the training examples. For binary class, SVM constructs a hyper-plane which can easily separate d-dimensional training examples perfectly into 2-classes. but sometimes, the training examples are not linearly separable. Thus, for non-linear training examples, SVM introduced Kernel functions which transforms the data into high dimensional space where the data can be separated linearly. For minimizing the test error and for improving classification accuracy, kernels functions are used. This paper explains applications of kernels in support vector machine and provide information about the properties of these kernels and situations in which they can be used.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124240082","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435759
M. Bhille, Namrata D. Hiremath
In real world many people face a lot of problem to contact other people while travelling and inform about their location details; in such cases this application solves the problem by first tracking the location of the person and then sending a text message to other person to the pre-set numbers periodically. An attempt is been made to develop a vehicle for the purpose of detecting the location and position of the vehicle carrying people or any asset. This information can be sent to remote user through global system for mobile communication. It provides a remote monitoring system for secured transportation for local conveyances. The Proposed system is equipped with GPS & GSM enabled embedded system along with microcontroller in the vehicle. GPS-GSM module helps the remote user to continuously track the location of the vehicle and activity of the driver.
{"title":"Tele-monitoring vehicle system for secured travelling","authors":"M. Bhille, Namrata D. Hiremath","doi":"10.1109/ICCIC.2015.7435759","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435759","url":null,"abstract":"In real world many people face a lot of problem to contact other people while travelling and inform about their location details; in such cases this application solves the problem by first tracking the location of the person and then sending a text message to other person to the pre-set numbers periodically. An attempt is been made to develop a vehicle for the purpose of detecting the location and position of the vehicle carrying people or any asset. This information can be sent to remote user through global system for mobile communication. It provides a remote monitoring system for secured transportation for local conveyances. The Proposed system is equipped with GPS & GSM enabled embedded system along with microcontroller in the vehicle. GPS-GSM module helps the remote user to continuously track the location of the vehicle and activity of the driver.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116044079","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435817
M. Aljohani, Tanweer Alam
Android smart devices are ubiquitous in our daily life and becoming valuable device with the capabilities of wireless networking that are typically used with an IEEE 802.11 access point. Ad hoc network provides facilities to access devices in infrastructure less system without a centralized approach. Android smart devices are dynamically joined and create an ad hoc network on their own. Android based smart configuration don't need centralized infrastructure for making connection in own created network. smart devices creates the network and start communication. M-learning is a key point for effective learning in this network. The main goal of this paper is to design a new M-learning framework for smart learning in Ad Hoc Network architecture among Android based Wi-Fi devices. Android is an open platform system with the feature of wireless. In our research we select Android and develop Android application for M-Learning through android based smart devices. we developed Android application for connecting android devices together in an ad hoc network without centralized approach. M-learning System has been implemented and results in Wi-Fi and Bluetooth were collected. The proposed system have been tested in android ad hoc network system. Application has been figured in Wi-Fi and Bluetooth ad hoc network environment.
{"title":"Design an M-learning framework for smart learning in ad hoc network of Android devices","authors":"M. Aljohani, Tanweer Alam","doi":"10.1109/ICCIC.2015.7435817","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435817","url":null,"abstract":"Android smart devices are ubiquitous in our daily life and becoming valuable device with the capabilities of wireless networking that are typically used with an IEEE 802.11 access point. Ad hoc network provides facilities to access devices in infrastructure less system without a centralized approach. Android smart devices are dynamically joined and create an ad hoc network on their own. Android based smart configuration don't need centralized infrastructure for making connection in own created network. smart devices creates the network and start communication. M-learning is a key point for effective learning in this network. The main goal of this paper is to design a new M-learning framework for smart learning in Ad Hoc Network architecture among Android based Wi-Fi devices. Android is an open platform system with the feature of wireless. In our research we select Android and develop Android application for M-Learning through android based smart devices. we developed Android application for connecting android devices together in an ad hoc network without centralized approach. M-learning System has been implemented and results in Wi-Fi and Bluetooth were collected. The proposed system have been tested in android ad hoc network system. Application has been figured in Wi-Fi and Bluetooth ad hoc network environment.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123634763","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435675
Gangatharan Kumarasamy, N. Prakash, P. Mohan
This paper discusses on the design and use of emerging technologies to provide a rider assistance system with an integrated safety mechanism for two wheelers (2W). This paper identifies the most frequent causes of two wheeler accidents and how the rider interacts with the vehicle during the pre-crash phase and vehicle behavior by analyzing the two wheeler accident data. Rider Assistance System (RAS) shall identify the existing technologies and safety systems in passenger cars and assess their usability or with modifications in two wheelers. This system shall continuously monitor the approaching path for any stationary or moving obstacles. And also monitors the rider's field of vision for any deviation from the intended path. These inputs shall be achieved either by range sensor based technology or/and a computer vision based technology. These input parameters shall be processed by an Electronic Control Unit (ECU) to take an autonomous decision to avoid a crash or to warn the rider to take an action. The autonomous decision shall be de-accelerating the vehicle by intervening the rider's acceleration inputs. With this autonomous action of the system, a possible crash shall be prevented ensuring rider's safety.
{"title":"Rider assistance system with an active safety mechanism","authors":"Gangatharan Kumarasamy, N. Prakash, P. Mohan","doi":"10.1109/ICCIC.2015.7435675","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435675","url":null,"abstract":"This paper discusses on the design and use of emerging technologies to provide a rider assistance system with an integrated safety mechanism for two wheelers (2W). This paper identifies the most frequent causes of two wheeler accidents and how the rider interacts with the vehicle during the pre-crash phase and vehicle behavior by analyzing the two wheeler accident data. Rider Assistance System (RAS) shall identify the existing technologies and safety systems in passenger cars and assess their usability or with modifications in two wheelers. This system shall continuously monitor the approaching path for any stationary or moving obstacles. And also monitors the rider's field of vision for any deviation from the intended path. These inputs shall be achieved either by range sensor based technology or/and a computer vision based technology. These input parameters shall be processed by an Electronic Control Unit (ECU) to take an autonomous decision to avoid a crash or to warn the rider to take an action. The autonomous decision shall be de-accelerating the vehicle by intervening the rider's acceleration inputs. With this autonomous action of the system, a possible crash shall be prevented ensuring rider's safety.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123722472","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435769
C. S. Datta, G. Prasad, V. Shiva, Prasad Nayak, G. Bhargav
This paper discusses the design and analysis of a 16-bit 10MHz pipeline Analog to Digital Converter (ADC). A system and circuit level design of each component of the ADC was created in Cadence. Features of ADC were simulated in Matlab to test and examine its basic functionality. Transient analysis of the design was conducted to verify the performance of the ADC. Methods to correct non-linarites were identified and investigated. The goal of this Major Qualifying Project is to design and fabricate a 16-bit 10MHz Pipeline Analog to Digital Converter (ADC) using 0.25μm CMOS. The motivation for designing a Pipeline ADC comes from the desire to characterize and test the functionality of the novel "Split ADC" Architecture concept [3] using a non-algorithmic ADC. We successfully characterized the System-level functionality of a Pipeline ADC by simulating its features through Matlab. A major part of the analog subsystem of the ADC was designed in Cadence. The simulation work corroborates with our theory and helped us to analyze the design block. It has provided us an opportunity to compare and contrast the ideal and non-ideal behavior of an ADC. Once the layout of the IC has been designed and fabricated, we shall move on to further work needed for data acquisition using a software package similar to LabView or Python.
{"title":"Design and analysis of a 16-bit 10MHz pipeline ADC in 0.25μ CMOS","authors":"C. S. Datta, G. Prasad, V. Shiva, Prasad Nayak, G. Bhargav","doi":"10.1109/ICCIC.2015.7435769","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435769","url":null,"abstract":"This paper discusses the design and analysis of a 16-bit 10MHz pipeline Analog to Digital Converter (ADC). A system and circuit level design of each component of the ADC was created in Cadence. Features of ADC were simulated in Matlab to test and examine its basic functionality. Transient analysis of the design was conducted to verify the performance of the ADC. Methods to correct non-linarites were identified and investigated. The goal of this Major Qualifying Project is to design and fabricate a 16-bit 10MHz Pipeline Analog to Digital Converter (ADC) using 0.25μm CMOS. The motivation for designing a Pipeline ADC comes from the desire to characterize and test the functionality of the novel \"Split ADC\" Architecture concept [3] using a non-algorithmic ADC. We successfully characterized the System-level functionality of a Pipeline ADC by simulating its features through Matlab. A major part of the analog subsystem of the ADC was designed in Cadence. The simulation work corroborates with our theory and helped us to analyze the design block. It has provided us an opportunity to compare and contrast the ideal and non-ideal behavior of an ADC. Once the layout of the IC has been designed and fabricated, we shall move on to further work needed for data acquisition using a software package similar to LabView or Python.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124729739","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435711
R. Delshi Howsalya Devi, P. Deepika
Breast Cancer is a decisive disease when compared to all other cancers in worldwide. Diagnosis of breast cancer is normally clinical and biological in nature. In general we used some of the data mining clustering techniques to predict breast cancer. The objective of this paper is to compare the performance of different Clustering techniques to diagnosis the cancer either benign or malignant. According to the results of our experimental work, we compared five clustering techniques such as DBSCAN, Farthest first, canopy, LVQ and hierarchical clustering in Weka software and comparison results show that Farthest First clustering has higher prediction accuracy i.e. 72% than DBSCAN, Canopy, LVQ and Hierarchical clustering methods.
{"title":"Performance comparison of various clustering techniques for diagnosis of breast cancer","authors":"R. Delshi Howsalya Devi, P. Deepika","doi":"10.1109/ICCIC.2015.7435711","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435711","url":null,"abstract":"Breast Cancer is a decisive disease when compared to all other cancers in worldwide. Diagnosis of breast cancer is normally clinical and biological in nature. In general we used some of the data mining clustering techniques to predict breast cancer. The objective of this paper is to compare the performance of different Clustering techniques to diagnosis the cancer either benign or malignant. According to the results of our experimental work, we compared five clustering techniques such as DBSCAN, Farthest first, canopy, LVQ and hierarchical clustering in Weka software and comparison results show that Farthest First clustering has higher prediction accuracy i.e. 72% than DBSCAN, Canopy, LVQ and Hierarchical clustering methods.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124849339","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435630
S. Lalitha, Anoop Mudupu, Bala Visali Nandyala, Renuka Munagala
Emotion recognition from speech helps us in improving the effectiveness of human-machine interaction. This paper presents a method to identify suitable features in DWT domain and improve good accuracy. In this work, 7 emotions (Berlin Database) are recognized using Support Vector Machine (SVM) classifier. Entropy of Teager Energy operated Discrete Wavelet Transform (DWT) coefficients, Linear Predictive Cepstral Coefficients(LPCC), Mel Energy Spectral Dynamic Coefficients(MEDC), Zero Crossing Rate (ZCR), shimmer, spectral roll off, spectral flux, spectral centroid, pitch, short time energy and Harmonic to Noise Ratio (HNR) are considered as features. The obtained average accuracy is 82.14 % Earlier work done on emotion recognition using DWT coefficients yielded an accuracy of 63.63 % and 68.5% for 4 emotions on Berlin and Malayalam databases respectively. The proposed algorithm shows a significant increase in accuracy of about 15% to 20% for 7 emotions on Berlin database. Also, 100% efficiency has been achieved for four emotions with Simple Logistic classifier of WEKA 3.6 tool.
{"title":"Speech emotion recognition using DWT","authors":"S. Lalitha, Anoop Mudupu, Bala Visali Nandyala, Renuka Munagala","doi":"10.1109/ICCIC.2015.7435630","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435630","url":null,"abstract":"Emotion recognition from speech helps us in improving the effectiveness of human-machine interaction. This paper presents a method to identify suitable features in DWT domain and improve good accuracy. In this work, 7 emotions (Berlin Database) are recognized using Support Vector Machine (SVM) classifier. Entropy of Teager Energy operated Discrete Wavelet Transform (DWT) coefficients, Linear Predictive Cepstral Coefficients(LPCC), Mel Energy Spectral Dynamic Coefficients(MEDC), Zero Crossing Rate (ZCR), shimmer, spectral roll off, spectral flux, spectral centroid, pitch, short time energy and Harmonic to Noise Ratio (HNR) are considered as features. The obtained average accuracy is 82.14 % Earlier work done on emotion recognition using DWT coefficients yielded an accuracy of 63.63 % and 68.5% for 4 emotions on Berlin and Malayalam databases respectively. The proposed algorithm shows a significant increase in accuracy of about 15% to 20% for 7 emotions on Berlin database. Also, 100% efficiency has been achieved for four emotions with Simple Logistic classifier of WEKA 3.6 tool.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"44 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129680150","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435739
Rishikesh J. Teke, M. Chaudhari, R. Prasad
The Mobile Ad-hoc Networks (MANET) are suffering from network partitioning when there is group mobility and thus cannot efficiently provide connectivity to all nodes in the network. Autonomous Mobile Mesh Network (AMMNET) is a new class of MANET which will overcome the weakness of MANET, especially from network partitioning. However, AMMNET is vulnerable to routing attacks such as Blackhole attack in which malicious node can make itself as intragroup, intergroup or intergroup bridge router and disrupt the network. In AMMNET, To maintain connectivity, network survivability is an important aspect of reliable communication. Maintaning security is a challenge in the self organising nature of the topology. To address this weakness proposed approach measured the performance of the impact of security enhancement on AMMNET with the basis of bait detection scheme. Modified bait approach that will prevent blackhole node entering into the network and helps to maintain the reliability of the network. The proposed scheme uses the idea of Wumpus World concept from Artificial Intelligence. Modified bait scheme will prevent the blackhole attack and secures network.
{"title":"Impact of security enhancement over Autonomous Mobile Mesh Network (AMMNET)","authors":"Rishikesh J. Teke, M. Chaudhari, R. Prasad","doi":"10.1109/ICCIC.2015.7435739","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435739","url":null,"abstract":"The Mobile Ad-hoc Networks (MANET) are suffering from network partitioning when there is group mobility and thus cannot efficiently provide connectivity to all nodes in the network. Autonomous Mobile Mesh Network (AMMNET) is a new class of MANET which will overcome the weakness of MANET, especially from network partitioning. However, AMMNET is vulnerable to routing attacks such as Blackhole attack in which malicious node can make itself as intragroup, intergroup or intergroup bridge router and disrupt the network. In AMMNET, To maintain connectivity, network survivability is an important aspect of reliable communication. Maintaning security is a challenge in the self organising nature of the topology. To address this weakness proposed approach measured the performance of the impact of security enhancement on AMMNET with the basis of bait detection scheme. Modified bait approach that will prevent blackhole node entering into the network and helps to maintain the reliability of the network. The proposed scheme uses the idea of Wumpus World concept from Artificial Intelligence. Modified bait scheme will prevent the blackhole attack and secures network.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132106295","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435656
B. A. Dakshitha, V. Deekshitha, K. Manikantan
Image segmentation requires optimum multilevel threshold values obtained from the image in order to partition it into multiple regions. Estimating these thresholds poses a great challenge. In this paper, we propose a novel swarm intelligence technique, namely Bi-level Artificial Bee Colony (BABC) algorithm, to obtain the optimum thresholds by using the Tsallis Entropy as an objective function. BABC is used, along with a Sinusoidal Evaluation of Fitness Function (SEFF), to ensure that all the threshold values of the image are examined before arriving at the best possible solution. Experimental results show the promising performance of BABC for image segmentation as compared to other optimization algorithms like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bacterial Foraging (BF) Algorithm.
{"title":"A novel Bi-level Artificial Bee Colony algorithm and its application to image segmentation","authors":"B. A. Dakshitha, V. Deekshitha, K. Manikantan","doi":"10.1109/ICCIC.2015.7435656","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435656","url":null,"abstract":"Image segmentation requires optimum multilevel threshold values obtained from the image in order to partition it into multiple regions. Estimating these thresholds poses a great challenge. In this paper, we propose a novel swarm intelligence technique, namely Bi-level Artificial Bee Colony (BABC) algorithm, to obtain the optimum thresholds by using the Tsallis Entropy as an objective function. BABC is used, along with a Sinusoidal Evaluation of Fitness Function (SEFF), to ensure that all the threshold values of the image are examined before arriving at the best possible solution. Experimental results show the promising performance of BABC for image segmentation as compared to other optimization algorithms like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bacterial Foraging (BF) Algorithm.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126693603","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 : 2015-12-01DOI: 10.1109/ICCIC.2015.7435788
P. Pranav, G. Jeyakumar
Differential Evolution (DE), an optimization algorithm under the roof of Evolutionary Algorithms (EAs), is well known for its efficiency in solving optimization problems which are non-linear and non-differentiable. DE has many good qualities such as algorithmic simplicity, robustness and reliability. DE also has the quality of solving the given problem with few control parameters (NP - population size, F - mutation rate and Cr - crossover rate). However, suitable setting of values to these parameters is a complicated task. Hence, various adaptation strategies to tune these parameters during the run of DE algorithm are proposed in the literature. Choosing the right adaptation strategy itself is another difficult task, which need in-depth understanding of existing adaptation strategies. The aim of this paper is to summarize various adaptation strategies proposed in DE literature for adapting F and Cr. The adaptation strategies are categorized based on the logic used by the authors for adaptation, and brief insights about each of the categories along with the corresponding adaptation strategies are presented.
{"title":"Control parameter adaptation strategies for mutation and crossover rates of differential evolution algorithm - An insight","authors":"P. Pranav, G. Jeyakumar","doi":"10.1109/ICCIC.2015.7435788","DOIUrl":"https://doi.org/10.1109/ICCIC.2015.7435788","url":null,"abstract":"Differential Evolution (DE), an optimization algorithm under the roof of Evolutionary Algorithms (EAs), is well known for its efficiency in solving optimization problems which are non-linear and non-differentiable. DE has many good qualities such as algorithmic simplicity, robustness and reliability. DE also has the quality of solving the given problem with few control parameters (NP - population size, F - mutation rate and Cr - crossover rate). However, suitable setting of values to these parameters is a complicated task. Hence, various adaptation strategies to tune these parameters during the run of DE algorithm are proposed in the literature. Choosing the right adaptation strategy itself is another difficult task, which need in-depth understanding of existing adaptation strategies. The aim of this paper is to summarize various adaptation strategies proposed in DE literature for adapting F and Cr. The adaptation strategies are categorized based on the logic used by the authors for adaptation, and brief insights about each of the categories along with the corresponding adaptation strategies are presented.","PeriodicalId":276894,"journal":{"name":"2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126508970","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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