H.4.7. Methodology and Techniques
Osman K. Erol; I. Eksin; A. Akdemir; A. Aydınoglu
Abstract
In general, all of the hybridized evolutionary optimization algorithms use “first diversification and then intensification” routine approach. In other words, these hybridized methods all begin with a global search mode using a highly random initial search population and then switch to intense ...
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In general, all of the hybridized evolutionary optimization algorithms use “first diversification and then intensification” routine approach. In other words, these hybridized methods all begin with a global search mode using a highly random initial search population and then switch to intense local search mode at some stage. The population initialization is still a crucial point in the hybridized evolutionary optimization algorithms since it can affect the speed of convergence and the quality of the final solution. In this study, we introduce a new approach by creating a paradigm shift that reverses the “diversification” and then “intensification” routines. Here, instead of starting from a random initial population, we firstly find a unique starting point by conducting an initial exhaustive search based on the coordinate exhaustive search local optimization algorithm only for single step iteration in order to collect a rough but some meaningful knowledge about the nature of the problem. Thus, our main assertion is that this approach will ameliorate convergence rate of any evolutionary optimization algorithms. In this study, we illustrate how one can use this unique starting point in the initialization of two evolutionary optimization algorithms, including but not limited to Big Bang-Big Crunch optimization and Particle Swarm Optimization. Experiments on a commonly used benchmark test suite, which consist of mainly rotated and shifted functions, show that the proposed initialization procedure leads to great improvement for the above-mentioned two evolutionary optimization algorithms.
I.3.7. Engineering
F. Nosratian; H. Nematzadeh; H. Motameni
Abstract
World Wide Web is growing at a very fast pace and makes a lot of information available to the public. Search engines used conventional methods to retrieve information on the Web; however, the search results of these engines are still able to be refined and their accuracy is not high enough. One of the ...
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World Wide Web is growing at a very fast pace and makes a lot of information available to the public. Search engines used conventional methods to retrieve information on the Web; however, the search results of these engines are still able to be refined and their accuracy is not high enough. One of the methods for web mining is evolutionary algorithms which search according to the user interests. The proposed method based on genetic algorithm optimizes important relationships among links on web pages and also presented a way for classifying web documents. Likewise, the proposed method also finds the best pages among searched ones by engines. Also, it calculates the quality of pages by web page features independently or dependently. The proposed algorithm is complementary to the search engines. In the proposed methods, after implementation of the genetic algorithm using MATLAB 2013 with crossover rate of 0.7 and mutation rate of 0.05, the best and the most similar pages are presented to the user. The optimal solutions remained fixed in several running of the proposed algorithm.
H.3.15.3. Evolutionary computing and genetic algorithms
H.R Keshavarz; M. Saniee Abadeh
Abstract
In Web 2.0, people are free to share their experiences, views, and opinions. One of the problems that arises in web 2.0 is the sentiment analysis of texts produced by users in outlets such as Twitter. One of main the tasks of sentiment analysis is subjectivity classification. Our aim is to classify the ...
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In Web 2.0, people are free to share their experiences, views, and opinions. One of the problems that arises in web 2.0 is the sentiment analysis of texts produced by users in outlets such as Twitter. One of main the tasks of sentiment analysis is subjectivity classification. Our aim is to classify the subjectivity of Tweets. To this end, we create subjectivity lexicons in which the words into objective and subjective words. To create these lexicons, we make use of three metaheuristic methods. We extract two meta-level features, which show the count of objective and subjective words in tweets according to the lexicons. We then classify the tweets based on these two features. Our method outperforms the baselines in terms of accuracy and f-measure. In the three metaheuristics, it is observed that genetic algorithm performs better than simulated annealing and asexual reproduction optimization, and it also outperforms all the baselines in terms of accuracy in two of the three assessed datasets. The created lexicons also give insight about the objectivity and subjectivity of words.