Pub Date : 2014-12-01DOI: 10.1109/FOCI.2014.7007802
Chee Ken Choy, K. Nguyen, R. Thawonmas
In this paper, we present an enhanced quantum-inspired genetic algorithm (eQiGA) with a combination of proposed mechanisms: two search supportive schemes and artificial entanglement. This combination is aimed at balancing exploration and exploitation. Two schemes, namely Explore and Exploit scheme are designed with aggressive specific roles reflecting its name. Entanglement is considered to be one of the significant strengths in quantum computing aside the probabilistic representation and superposition. Hence we attempt to apply its concept as part of our strategy for its potential. In addition, two new sub-strategies are proposed: fitness threshold, and quantum side-stepping. The algorithm is tested on multiple numerical optimization functions, and significant results of improved performance are obtained, studied, and discussed.
{"title":"Quantum-inspired genetic algorithm with two search supportive schemes and artificial entanglement","authors":"Chee Ken Choy, K. Nguyen, R. Thawonmas","doi":"10.1109/FOCI.2014.7007802","DOIUrl":"https://doi.org/10.1109/FOCI.2014.7007802","url":null,"abstract":"In this paper, we present an enhanced quantum-inspired genetic algorithm (eQiGA) with a combination of proposed mechanisms: two search supportive schemes and artificial entanglement. This combination is aimed at balancing exploration and exploitation. Two schemes, namely Explore and Exploit scheme are designed with aggressive specific roles reflecting its name. Entanglement is considered to be one of the significant strengths in quantum computing aside the probabilistic representation and superposition. Hence we attempt to apply its concept as part of our strategy for its potential. In addition, two new sub-strategies are proposed: fitness threshold, and quantum side-stepping. The algorithm is tested on multiple numerical optimization functions, and significant results of improved performance are obtained, studied, and discussed.","PeriodicalId":274407,"journal":{"name":"2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121667767","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 : 2014-12-01DOI: 10.1109/FOCI.2014.7007812
Gang Bao, Z. Zeng
Memristor is a nonlinear resistor with the character of memory and is proved to be suitable for simulating synapse of neuron. This paper introduces two memristors in series with the same polarity (back-to-back) as simulator for neuron's synapse and presents the model of recurrent neural networks with such back-to-back memristors. By analysis techniques and fixed point theory, some sufficient conditions are obtained for recurrent neural network having single attractor flow and multiple attractors flow. At last, simulation with numeric examples is presented to illustrate our results.
{"title":"Attractor flow analysis for recurrent neural network with back-to-back memristors","authors":"Gang Bao, Z. Zeng","doi":"10.1109/FOCI.2014.7007812","DOIUrl":"https://doi.org/10.1109/FOCI.2014.7007812","url":null,"abstract":"Memristor is a nonlinear resistor with the character of memory and is proved to be suitable for simulating synapse of neuron. This paper introduces two memristors in series with the same polarity (back-to-back) as simulator for neuron's synapse and presents the model of recurrent neural networks with such back-to-back memristors. By analysis techniques and fixed point theory, some sufficient conditions are obtained for recurrent neural network having single attractor flow and multiple attractors flow. At last, simulation with numeric examples is presented to illustrate our results.","PeriodicalId":274407,"journal":{"name":"2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130188495","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 : 2014-12-01DOI: 10.1109/FOCI.2014.7007801
Upul Senanayake, Piraveenan Mahendra, Albert Y. Zomaya
The h-index is a very well known metric used to measure scientific throughput, but it also has well known limitations. In this paper we use a metric based on pagerank algorithm, which we call the p-index, to compare the performance of scientists. We use a real-world dataset to which we apply our analysis: a dataset of scientists from the field of quantum game theory. This dataset is cured by us for this study from Google Scholar. We show that whereas the popularly used h-index rewards authors who collaborate extensively and publish in higher volumes, the p-index rewards hardworking authors who contribute more to each paper they write, as well as authors who publish in high impact and well cited journals. As such, it could be argued that the p-index is a `fairer' metric of the productivity and impact of scientists. Of particular note is that the p-index does not use the so called `impact factors' of journals, the utility of which is debated ins scientific community. Rather, the p-index relies on the actual underlying citation network to measure the real impact of each paper. Furthermore, the p-index relies not only on the number of citations but also on the quality of citations of each paper. Using p-index, we highlight and compare the impact of real world scientists on the field of quantum game theory.
{"title":"Ranking scientists from the field of quantum game theory using p-index","authors":"Upul Senanayake, Piraveenan Mahendra, Albert Y. Zomaya","doi":"10.1109/FOCI.2014.7007801","DOIUrl":"https://doi.org/10.1109/FOCI.2014.7007801","url":null,"abstract":"The h-index is a very well known metric used to measure scientific throughput, but it also has well known limitations. In this paper we use a metric based on pagerank algorithm, which we call the p-index, to compare the performance of scientists. We use a real-world dataset to which we apply our analysis: a dataset of scientists from the field of quantum game theory. This dataset is cured by us for this study from Google Scholar. We show that whereas the popularly used h-index rewards authors who collaborate extensively and publish in higher volumes, the p-index rewards hardworking authors who contribute more to each paper they write, as well as authors who publish in high impact and well cited journals. As such, it could be argued that the p-index is a `fairer' metric of the productivity and impact of scientists. Of particular note is that the p-index does not use the so called `impact factors' of journals, the utility of which is debated ins scientific community. Rather, the p-index relies on the actual underlying citation network to measure the real impact of each paper. Furthermore, the p-index relies not only on the number of citations but also on the quality of citations of each paper. Using p-index, we highlight and compare the impact of real world scientists on the field of quantum game theory.","PeriodicalId":274407,"journal":{"name":"2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124522302","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 : 2014-12-01DOI: 10.1109/FOCI.2014.7007811
Kostas Hatalis, Basel Alnajjab, S. Kishore, A. Lamadrid
In this study we propose the development of an adaptive particle swarm optimization (APSO) learning algorithm to train a non-linear autoregressive (NAR) neural network, which we call PSONAR, for short term time series prediction of ocean wave elevations. We also introduce a new stochastic inertial weight to the APSO learning algorithm. Our work is motivated by the expected need for such predictions by wave energy farms. In particular, it has been shown that the phase resolved predictions provided in this paper could be used as inputs to novel control methods that hold promise to at least double the current efficiency of wave energy converter (WEC) devices. As such, we simulated noisy ocean wave heights for testing. We utilized our PSONAR to get results for 5, 10, 30, and 60 second multistep predictions. Results are compared to a standard backpropagation model. Results show APSO can outperform backpropagation in training a NAR neural network.
{"title":"Adaptive particle swarm optimization learning in a time delayed recurrent neural network for multi-step prediction","authors":"Kostas Hatalis, Basel Alnajjab, S. Kishore, A. Lamadrid","doi":"10.1109/FOCI.2014.7007811","DOIUrl":"https://doi.org/10.1109/FOCI.2014.7007811","url":null,"abstract":"In this study we propose the development of an adaptive particle swarm optimization (APSO) learning algorithm to train a non-linear autoregressive (NAR) neural network, which we call PSONAR, for short term time series prediction of ocean wave elevations. We also introduce a new stochastic inertial weight to the APSO learning algorithm. Our work is motivated by the expected need for such predictions by wave energy farms. In particular, it has been shown that the phase resolved predictions provided in this paper could be used as inputs to novel control methods that hold promise to at least double the current efficiency of wave energy converter (WEC) devices. As such, we simulated noisy ocean wave heights for testing. We utilized our PSONAR to get results for 5, 10, 30, and 60 second multistep predictions. Results are compared to a standard backpropagation model. Results show APSO can outperform backpropagation in training a NAR neural network.","PeriodicalId":274407,"journal":{"name":"2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126171823","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 : 2014-12-01DOI: 10.1109/FOCI.2014.7007809
M. Epitropakis, Fabio Caraffini, Ferrante Neri, E. Burke
One of the main challenges in algorithmics in general, and in Memetic Computing, in particular, is the automatic design of search algorithms. A recent advance in this direction (in terms of continuous problems) is the development of a software prototype that builds up an algorithm based upon a problem analysis of its separability. This prototype has been called the Separability Prototype for Automatic Memes (SPAM). This article modifies the SPAM by incorporating within it an adaptive model used in hyper-heuristics for tackling optimization problems. This model, namely Adaptive Operator Selection (AOS), rewards at run time the most promising heuristics/memes so that they are more likely to be used in the following stages of the search process. The resulting framework, here referred to as SPAM-AOS, has been tested on various benchmark problems and compared with modern algorithms representing the-state-of-the-art of search for continuous problems. Numerical results show that the proposed SPAM-AOS is a promising framework that outperforms the original SPAM and other modern algorithms. Most importantly, this study shows how certain areas of Memetic Computing and Hyper-heuristics are very closely related topics and it also shows that their combination can lead to the development of powerful algorithmic frameworks.
{"title":"A Separability Prototype for Automatic Memes with Adaptive Operator Selection","authors":"M. Epitropakis, Fabio Caraffini, Ferrante Neri, E. Burke","doi":"10.1109/FOCI.2014.7007809","DOIUrl":"https://doi.org/10.1109/FOCI.2014.7007809","url":null,"abstract":"One of the main challenges in algorithmics in general, and in Memetic Computing, in particular, is the automatic design of search algorithms. A recent advance in this direction (in terms of continuous problems) is the development of a software prototype that builds up an algorithm based upon a problem analysis of its separability. This prototype has been called the Separability Prototype for Automatic Memes (SPAM). This article modifies the SPAM by incorporating within it an adaptive model used in hyper-heuristics for tackling optimization problems. This model, namely Adaptive Operator Selection (AOS), rewards at run time the most promising heuristics/memes so that they are more likely to be used in the following stages of the search process. The resulting framework, here referred to as SPAM-AOS, has been tested on various benchmark problems and compared with modern algorithms representing the-state-of-the-art of search for continuous problems. Numerical results show that the proposed SPAM-AOS is a promising framework that outperforms the original SPAM and other modern algorithms. Most importantly, this study shows how certain areas of Memetic Computing and Hyper-heuristics are very closely related topics and it also shows that their combination can lead to the development of powerful algorithmic frameworks.","PeriodicalId":274407,"journal":{"name":"2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122371407","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 : 2014-12-01DOI: 10.1109/FOCI.2014.7007817
J. Anochi, H. Velho
Optimization of neural network topology, weights and neuron activation functions for given data set and problem is not an easy task. In this article, a technique for automatic configuration of parameters topology for feedforward artificial neural networks (ANN) is presented. The determination of optimal parameters is formulated as an optimization problem, solved with the use of meta-heuristic Multiple Particle Collision Algorithm (MPCA). The self-configuring networks are applied to predict the mesoscale climate for the precipitation field. The results obtained from the neural network using the method of data reduction by the Theory of Rough Sets and the self-configuring network by MPCA were compared.
{"title":"Optimization of feedforward neural network by Multiple Particle Collision Algorithm","authors":"J. Anochi, H. Velho","doi":"10.1109/FOCI.2014.7007817","DOIUrl":"https://doi.org/10.1109/FOCI.2014.7007817","url":null,"abstract":"Optimization of neural network topology, weights and neuron activation functions for given data set and problem is not an easy task. In this article, a technique for automatic configuration of parameters topology for feedforward artificial neural networks (ANN) is presented. The determination of optimal parameters is formulated as an optimization problem, solved with the use of meta-heuristic Multiple Particle Collision Algorithm (MPCA). The self-configuring networks are applied to predict the mesoscale climate for the precipitation field. The results obtained from the neural network using the method of data reduction by the Theory of Rough Sets and the self-configuring network by MPCA were compared.","PeriodicalId":274407,"journal":{"name":"2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127579409","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 : 2014-12-01DOI: 10.1109/FOCI.2014.7007805
D. Ashlock, J. Schonfeld, Lee-Ann Barlow, Colin Lee
Graph evolution - evolving a graph or network to fit specific criteria - is a recent enterprise because of the difficulty of representing a graph in an easily evolvable form. Simple, obvious representations such as adjacency matrices can prove to be very hard to evolve and some easy-to-evolve representations place severe limits on the space of graphs that is explored. This study fills in a gap in the literature by presenting two scalable families of benchmark functions. These functions are tested on a number of representations. The first family of benchmark functions is matching the eccentricity sequences of graphs, the second is locating graphs that are relatively easy to color non-optimally. One hundred examples of the eccentricity sequence matching problem are tested. The examples have a difficulty, measured in time to solution, that varies through four orders of magnitude, demonstrating that this test problem exhibits scalability even within a particular size of problem. The ordering by problem hardness, for different representations, varies significantly from representation to representation. For the difficult coloring problem, a parameter study is presented demonstrating that the problem exhibits very different results for different algorithm parameters, demonstrating its effectiveness as a benchmark problem.
{"title":"Test problems and representations for graph evolution","authors":"D. Ashlock, J. Schonfeld, Lee-Ann Barlow, Colin Lee","doi":"10.1109/FOCI.2014.7007805","DOIUrl":"https://doi.org/10.1109/FOCI.2014.7007805","url":null,"abstract":"Graph evolution - evolving a graph or network to fit specific criteria - is a recent enterprise because of the difficulty of representing a graph in an easily evolvable form. Simple, obvious representations such as adjacency matrices can prove to be very hard to evolve and some easy-to-evolve representations place severe limits on the space of graphs that is explored. This study fills in a gap in the literature by presenting two scalable families of benchmark functions. These functions are tested on a number of representations. The first family of benchmark functions is matching the eccentricity sequences of graphs, the second is locating graphs that are relatively easy to color non-optimally. One hundred examples of the eccentricity sequence matching problem are tested. The examples have a difficulty, measured in time to solution, that varies through four orders of magnitude, demonstrating that this test problem exhibits scalability even within a particular size of problem. The ordering by problem hardness, for different representations, varies significantly from representation to representation. For the difficult coloring problem, a parameter study is presented demonstrating that the problem exhibits very different results for different algorithm parameters, demonstrating its effectiveness as a benchmark problem.","PeriodicalId":274407,"journal":{"name":"2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127864438","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 : 2014-12-01DOI: 10.1109/FOCI.2014.7007818
W. Ashlock, Jeffrey Tsang, D. Ashlock
The evolution of cooperation has been much studied in the context of the game of iterated prisoner's dilemma. This paper examines, instead, the evolution of exploitation, strategies that succeed at the expense of their opponent. Exploitation is studied when opponents are close kin, against other evolved strategies, and against arbitrary strategies. A representation for strategies, called shaped prisoner's dilemma automata, is used to find exploitative strategies using a co-evolutionary algorithm. This representation alters both the space of strategies searched and the connectivity of that space. Eight different shapes are studied in the context of their ability to find exploitative strategies.
{"title":"The evolution of exploitation","authors":"W. Ashlock, Jeffrey Tsang, D. Ashlock","doi":"10.1109/FOCI.2014.7007818","DOIUrl":"https://doi.org/10.1109/FOCI.2014.7007818","url":null,"abstract":"The evolution of cooperation has been much studied in the context of the game of iterated prisoner's dilemma. This paper examines, instead, the evolution of exploitation, strategies that succeed at the expense of their opponent. Exploitation is studied when opponents are close kin, against other evolved strategies, and against arbitrary strategies. A representation for strategies, called shaped prisoner's dilemma automata, is used to find exploitative strategies using a co-evolutionary algorithm. This representation alters both the space of strategies searched and the connectivity of that space. Eight different shapes are studied in the context of their ability to find exploitative strategies.","PeriodicalId":274407,"journal":{"name":"2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124649158","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 : 2014-12-01DOI: 10.1109/FOCI.2014.7007806
G. Karafotias, M. Hoogendoorn, Berend Weel
Parameter controllers for Evolutionary Algorithms (EAs) deal with adjusting parameter values during an evolutionary run. Many ad hoc approaches have been presented for parameter control, but few generic parameter controllers exist and, additionally, no comparisons or in depth analyses of these generic controllers are available in literature. This paper presents an extensive comparison of such generic parameter control methods, including a number of novel controllers based on reinforcement learning which are introduced here. We conducted experiments with different EAs and test problems in an one-off setting, i.e. relatively long runs with controllers used out-of-the-box with no tailoring to the problem at hand. Results reveal several interesting insights regarding the effectiveness of parameter control, the niche applications/EAs, the effect of continuous treatment of parameters and the influence of noise and randomness on control.
{"title":"Comparing generic parameter controllers for EAs","authors":"G. Karafotias, M. Hoogendoorn, Berend Weel","doi":"10.1109/FOCI.2014.7007806","DOIUrl":"https://doi.org/10.1109/FOCI.2014.7007806","url":null,"abstract":"Parameter controllers for Evolutionary Algorithms (EAs) deal with adjusting parameter values during an evolutionary run. Many ad hoc approaches have been presented for parameter control, but few generic parameter controllers exist and, additionally, no comparisons or in depth analyses of these generic controllers are available in literature. This paper presents an extensive comparison of such generic parameter control methods, including a number of novel controllers based on reinforcement learning which are introduced here. We conducted experiments with different EAs and test problems in an one-off setting, i.e. relatively long runs with controllers used out-of-the-box with no tailoring to the problem at hand. Results reveal several interesting insights regarding the effectiveness of parameter control, the niche applications/EAs, the effect of continuous treatment of parameters and the influence of noise and randomness on control.","PeriodicalId":274407,"journal":{"name":"2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116408456","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 : 2014-12-01DOI: 10.1109/FOCI.2014.7007803
Upul Senanayake, Peter Szot, Piraveenan Mahendra, D. Kasthurirathna
Page rank is a ranking algorithm based on a random surfer model which is used in Google search engine and many other domains. Because of its initial success in Google search engine, page rank has become the de-facto choice when it comes to ranking nodes in a network structure. Despite the ubiquitous utility of the algorithm, little is known about the effect of topology on the performance of the page rank algorithm. Hence this paper discusses the performance of page rank algorithm under different topological conditions. We use scale-free networks and random networks along with a custom search engine we implemented in order to experimentally prove that the performance of page rank algorithm is deteriorated when the random network is perturbed. In contrast, scale-free topology is proven to be resilient against degree preserving perturbations which aids the page rank algorithm to deliver consistent results across multiple networks that are perturbed to varying proportions. Not only does the top ranking results emerge as stable nodes, but the overall performance of the algorithm is proven to be remarkably resilient which deepens our understanding about the risks in applying page rank algorithm without an initial analysis on the underlying network structure. The results conclusively suggests that while page rank algorithm can be applied to scale-free networks with relatively low risk, applying page rank algorithm to other topologies can be risky as well as misleading. Therefore, the success of the page rank algorithm in real world in search engines such as Google is at least partly due to the fact that the world wide web is a scale-free network. Since the world wide web is constantly evolving, we postulate that if the topological structure of the world wide web changes significantly so that it loses its scale-free nature to some extent, the page rank algorithm will not be as effective.
{"title":"The performance of page rank algorithm under degree preserving perturbations","authors":"Upul Senanayake, Peter Szot, Piraveenan Mahendra, D. Kasthurirathna","doi":"10.1109/FOCI.2014.7007803","DOIUrl":"https://doi.org/10.1109/FOCI.2014.7007803","url":null,"abstract":"Page rank is a ranking algorithm based on a random surfer model which is used in Google search engine and many other domains. Because of its initial success in Google search engine, page rank has become the de-facto choice when it comes to ranking nodes in a network structure. Despite the ubiquitous utility of the algorithm, little is known about the effect of topology on the performance of the page rank algorithm. Hence this paper discusses the performance of page rank algorithm under different topological conditions. We use scale-free networks and random networks along with a custom search engine we implemented in order to experimentally prove that the performance of page rank algorithm is deteriorated when the random network is perturbed. In contrast, scale-free topology is proven to be resilient against degree preserving perturbations which aids the page rank algorithm to deliver consistent results across multiple networks that are perturbed to varying proportions. Not only does the top ranking results emerge as stable nodes, but the overall performance of the algorithm is proven to be remarkably resilient which deepens our understanding about the risks in applying page rank algorithm without an initial analysis on the underlying network structure. The results conclusively suggests that while page rank algorithm can be applied to scale-free networks with relatively low risk, applying page rank algorithm to other topologies can be risky as well as misleading. Therefore, the success of the page rank algorithm in real world in search engines such as Google is at least partly due to the fact that the world wide web is a scale-free network. Since the world wide web is constantly evolving, we postulate that if the topological structure of the world wide web changes significantly so that it loses its scale-free nature to some extent, the page rank algorithm will not be as effective.","PeriodicalId":274407,"journal":{"name":"2014 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126502000","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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