Top PDF Genetic Algorithms Based Approach to Solve 0-1 Knapsack Problem Optimization Problem

Genetic Algorithms Based Approach to Solve 0-1 Knapsack Problem Optimization Problem

Genetic Algorithms Based Approach to Solve 0-1 Knapsack Problem Optimization Problem

ABSTRACT: In this paper, we solve 0-1 knapsack problem using genetic algorithm. The knapsack problem is also called the NP (non deterministic polynomial) problem. We have to maximize the profit value that can be put in to a knapsack under the confinement of its weight. Solve the knapsack problem and also show its possible and effectiveness crowd an example. The Genetic Algorithm uses corrupted renewal and focal improvement operators which are applied to every recent generated solution. Results show that most of the time the new Genetic Algorithm tend to the same point much faster to more appropriate results in particular for large problems. Genetic Algorithms are search approach based on natural selection and natural genetics. They erratically construct early residents of exclusive. They use genetic operators to concede offspring.
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APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK 
PROBLEM

APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK PROBLEM

In our modern digital society, enterprises have been forced to make rapid changes, called Cloud and Digital Transformation. Maintaining reputation by effectively managing risks and uncertainties pressures enterprises whose readiness cannot afford them. e-Commerce provides enterprise service-based internet platforms. These rapid changes have also manifested in the form of e-Commerce services; there is no doubt that sustaining a stable e-Commerce service is at the core of competitiveness in the coming digital transformation era. However, this involves architectural complexity. Thus we suggest a proactive approach that manages risks based on a machine learning algorithm, called anomaly detection. In particular, we propose a novel anomaly detection model that considers ordinal and multi-class cases that is effective in complex environments. Particularly, we propose OMSVM(Ordinal Multi-class Support Vector Machine) method for a multi-level e- Commerce anomaly detection. We also suggest a practical model evaluation method that exploits hidden information and provides numerical insights. Finally we discuss imbalanced data’s impact on multi-classes and solutions.
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APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK 
PROBLEM

APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK PROBLEM

In this paper, we presented a zero- watermarking method for CityGML model. The method filled in the gaps in the fields of CityGML copyright protection. The global geometry property vertex norm based method can resist several common attacks such as rotation, scaling, transform, vertex reordering, noise addition, simplification, and smoothing. However, the limited numbers of the points in the model object inhibit the capacity and the robustness of the watermark. Considering that each object in CityGML can have a different representation for every LOD, therefore, in the future, we will study the watermarking algorithms for objects with different LODs to enlarge the watermark capacity
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INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES MANAGEMENT A MODIFIED AND EFFICIENT GENETIC ALGORITHM TO SOLVE 0-1 KNAPSACK PROBLEM

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES MANAGEMENT A MODIFIED AND EFFICIENT GENETIC ALGORITHM TO SOLVE 0-1 KNAPSACK PROBLEM

value of the optimal solution that can be obtained by assigning F2 to the same bin. UGGA and genetic operators are guaranteed to generate individuals consisting only of un-dominated bin assignments. It also showed that UGGA significantly outperformed other algorithms for strongly correlated and multiple subset-sum problem but poorly performed on the weakly correlated instances. Similarly, the single- objective genetic algorithm (SOGAs) is compared with multi-objective genetic algorithms in the applications to multi- objective knapsack problems [11]. It has shown that MOGAs outperform SOGAs even when they are evaluated with respect to a scalar fitness function used in SOGAs. It also verifies that the search ability of MOGAs in inversely proportional to the number of objectives. Knapsack problem used as a class of compositional design problems is shown in [12]. The problem with complicated constraints is formulated as a set of local sub problems with simple constraints and a supervising problem. Every sub problem is solved by GA to generate a set of suboptimal solutions and in the supervising problem, the elements of each set are optimally combined by GA to yield the optimal solution for the original problem. The method is a learning method where the empirical knowledge obtained by solving the problem is effectively utilized to solve similar problems efficiently. A genetic algorithm in which the number of individuals changes to show increase in accuracy of solution is shown in [13]. The number of population is doubled initially against the accuracy of solution. First stage increases the searching ability. Then, accuracy is improved at second stage by reducing the number of population. Wei, beibel and jiang derived an improved solution for 0-1 knapsack problem based on
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An Algorithm of 0 1 Knapsack Problem  Based on Economic Model

An Algorithm of 0 1 Knapsack Problem Based on Economic Model

In order to optimize the knapsack problem further, this paper proposes an innovative model based on dynamic expecta- tion efficiency, and establishes a new optimization algorithm of 0-1 knapsack problem after analysis and research. Through analyzing the study of 30 groups of 0-1 knapsack problem from discrete coefficient of the data, we can find that dynamic expectation model can solve the following two types of knapsack problem. Compared to artificial glow- worm swam algorithm, the convergence speed of this algorithm is ten times as fast as that of artificial glowworm swam algorithm, and the storage space of this algorithm is one quarter that of artificial glowworm swam algorithm. To sum up, it can be widely used in practical problems.
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APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK 
PROBLEM

APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK PROBLEM

In this context, this paper develops refined FSWGAs and applies them to solve one of well- known combinatorial optimization problem with many feasible solutions, classical 0-1 KP. The experiment results demonstrate that the search strategy of FSWGA is also useful even if given problem has many feasible solutions. In addition, this paper suggests that the fitness switching procedure is the most important element of FSWGA, and fitness s  ( ) must be carefully chosen.

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APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK 
PROBLEM

APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK PROBLEM

To assess the research model in Figure 1, a self-administered survey approach was utilized to collect data from virtual reality service users in Seoul Metropolitan and the regional city. Participants were asked to indicate on a seven-point scale the degree to which they agreed with the statements. They were told that the survey was voluntary and their responses would be kept anonymous. To identify demographics of the respondents, a frequency analysis was performed based on a total of 217 samples. The samples chosen for this research were undergraduate students at H University in Seoul Metropolitan and at K University in Jeonbuk Province. Drennan et al. argued that university students are “representatives of a dominant cohort of online users” including virtual reality users. [9] Most of them were experienced and frequent users of the virtual reality services. This research verified reliability and validity of the model. And frequency analysis, T- test and multiple regression analysis were conducted.
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APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK 
PROBLEM

APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK PROBLEM

This study seeks new methods for developing educational materials using web-based multimedia tools in Korean language education. For this purpose, this study focuses on ‘Prezi’, a presentation tool which enables storytelling approaches. ‘Prezi’ is a cloud-based presentation tool, which features the interface that shifts pages using zoom-in effect. It breaks from the usual two-dimensional presentation to a more effective presentation with dynamic screen composition using space movement. Moreover, ‘Prezi’ enables a more comfortable visualization of digital storytelling. Digital storytelling allows nonlinear writing using the flexibility of the media, and anyone can become a director with the development of various multimedia technology. Moreover, the barrier between creators and audiences collapses and everyone can become participants of the contents due to the same digital media they share that allows networking. ‘Prezi’ presents the aforementioned advantages of digital storytelling and the advantages can be integrated in education by using ‘Prezi’ to develop educational materials. Even more, ‘Prezi’ can be utilized anywhere connected through the internet, and various people can access the educational materials by sharing URLs. This study will show the process and system of developing web/computer-based educational materials using ‘Prezi’ and will also present its actuality as e-learning material.
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Abstract The 0/1 Multiple Knapsack Problem is an

Abstract The 0/1 Multiple Knapsack Problem is an

Abstract— The 0/1 Multiple Knapsack Problem is an important class of combinatorial optimization problems, and various heuristic and exact methods have been devised to solve it. Genetic Algorithm (GA) shows good performance on solving static optimization problems. However, sometimes lost of diversity makes GA fail adapt to dynamic environments where evaluation function and/or constraints or environmental conditions may change over time. Several approaches have been developed for increasing the diversity of GA into dynamic environments. This paper compares two of these approaches named Random Immigrants Based GA (RIGA) and Memory Based GA (MBGA). Results show that MBGA is more effective than RIGA for The 0/1 Multiple Knapsack Problem in a changing environment.
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APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK 
PROBLEM

APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK PROBLEM

Students use analytical and logical thinking to approach and interpret problems in programming education. In the process of developing a program, students engage the creative thinking process to develop ideas, critical and convergent thinking to compare these ideas and choose the best option, and logical thinking to properly express computer languages. Additionally, students receive immediate feedback on their problem-solving through the resulting program. If there is an error, they ruminate on their thought process to logically analyze, and solve the error. Alternately, students can think creatively to consider the problem from new perspectives, breaking away from fixed ideas to resolve the error [1].
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HYBRID OPTIMIZATION FOR GRID SCHEDULING USING GENETIC ALGORITHM WITH LOCAL 
SEARCH

HYBRID OPTIMIZATION FOR GRID SCHEDULING USING GENETIC ALGORITHM WITH LOCAL SEARCH

Every algorithm has strength and weakness. With the description in previous section, we know that greedy algorithm is a fast algorithm but sometimes the greedy solution only approach the global solution. PSO is an algorithm that based on the best particle in its population. Because in PSO, the other particles in the population converge towards the best particle’s position. The better particle’s position, the faster PSO solves a problem. Genetic operators, like crossover and mutation are used to vary the solution. So, we put greedy solution to PSO initial population in the hope it can make PSO population better. Then add some genetic operators (crossover and mutation) in the hope it can find solution which is too far away from PSO population. The flowchart of GPSOGA can be seen at Figure 1.
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Neural, Genetic, And Neurogenetic Approaches For Solving The 0-1 Multidimensional Knapsack Problem

Neural, Genetic, And Neurogenetic Approaches For Solving The 0-1 Multidimensional Knapsack Problem

The multi-dimensional knapsack problem (MDKP) is a well-studied problem in Decision Sciences. The problem’s NP-Hard nature prevents the successful application of exact procedures such as branch and bound, implicit enumeration and dynamic programming for larger problems. As a result, various approximate solution approaches, such as the relaxation approaches, heuristic and metaheuristic approaches have been developed and applied effectively to this problem. In this study, we propose a Neural approach, a Genetic Algorithms approach and a Neurogenetic approach, which is a hybrid of the Neural and the Genetic Algorithms approach. The Neural approach is essentially a problem-space based non-deterministic local-search algorithm. In the Genetic Algorithms approach we propose a new way of generating initial population. In the Neurogenetic approach, we show that the Neural and Genetic iterations, when interleaved appropriately, can complement each other and provide better solutions than either the Neural or the Genetic approach alone. Within the overall search, the Genetic approach provides diversification while the Neural provides intensification. We demonstrate the effectiveness of our proposed approaches through an empirical study performed on several sets of benchmark problems commonly used in the literature.
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Genetic Algorithm based Approach to Solve Non Fractional (0/1) Knapsack Optimization Problem

Genetic Algorithm based Approach to Solve Non Fractional (0/1) Knapsack Optimization Problem

generation would contain stronger (fitter) individuals in contrast to its ancestors. The process of GA’s is iteration based of constant population size of candidate solutions. In each generation/iteration each chromosome’s fitness in the current population is evaluated and new population evolves. Chromosomes with higher fitness values goes through reproduction phase in which selection, crossover and mutation operators are applied to get new population. Chromosomes with lower fitness values are discarded. Again this generated new population is evaluated and selection, crossover, mutation operators are applied. This process continues until we get an optimal solution for the given problem
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Using Parallel Filtering Algorithms to Solve the 0 1 Knapsack Problem on DNA based Computing

Using Parallel Filtering Algorithms to Solve the 0 1 Knapsack Problem on DNA based Computing

q-bit binary number corresponds to a subset. An n-bit binary number encodes the size of weight for an element in S. Therefore, (q + 1) × n bits correspond to the sum of weight for q elements, and one accumulator element ( α ). q × (n + 1) bits encode the carry of the sum. A value sequence for every bit contains 15 bases. Therefore, the length of a DNA strand, encoding the total weight of selected items, is 15 × n base pairs consisting of the concatenation of one value sequence for each bit.

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APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK 
PROBLEM

APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK PROBLEM

In the aspect of developing cataracts, it will be important to detect the diagnosis early to reduce long-term problems through screening test. At the point of view, this research demonstrates the application of model to apply an easy, quick, and precise method for diagnosing early cataract based on machine learning algorithms, especially the ME model, by using a hospital screening center data. This study shows that ME model achieves superior performances compared to classical logistic regress ion model for the hospital screening datasets and the obtained results show that further significant feasibility of ME model in terms of hospital screening data can be applied.
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APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK 
PROBLEM

APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK PROBLEM

Attarodi et al. [6] proposed IPFM integral-model using four inputs of sinusoidal signals to simulate three-prominent peaks in the power spectrum of HRV. However, this IPFM model yielded the power spectrum of HRV with some spectral leakages between main waves. Thus, we propose an empirical IPFM model by employing data mining approach on MIT-BIH database.

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APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK 
PROBLEM

APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK PROBLEM

The main purpose of the study is to understand the relationship between people’s perception and participation regarding a future event. The analysis was performed to investigate whether the participation in Australian working holiday program changes due to the change in the perception of the people of Korea. In the research, we utilized time series statistical data and a set of sentiment data collected from a social platform. We used the number of Korean people who entered and exited Australia during the last 10 years and sentiment data from two social blogs, called cafes: 'I Love Australia' and 'Hoggoksung', having registered members of 150,500 and 110,127, respectively. The total numbers of posts were 169,827 and 196,217, respectively. In was found that the change in perception has strong impacts on the number of people participating in the program. We also found that the sentiment data-based prediction is much more effective than the simple numerical data-based prediction.
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APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK 
PROBLEM

APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK PROBLEM

A word has various meanings based on POS(Part-of-Speech) in the sentence. For example, as the word "good" is used, As we use it as an adjective or a noun, the polarity of the each positive or negative meaning is also different. Thus, it is necessary to extract the POS information in order to analyze the positive or negative polarity with respect to customers' reviews. In this study, POS tagging tool from "www.TextAnalysisOnline.com" was taken advantage of in order to extract the POS information of each word. TextAnalysis API(Application Programming Interface) provides customized Text Mining Services such as POS Tagging, Stemmer, Word Tokenize, Lemmatizer, Chunker, Parser, Key Phrase Extraction(Noun Phrase Extraction), Sentence Segmentation (Sentence Boundary Detection), Sentiment Analysis, Text Summarizer, Grammar Checker, Text Classifier and other Text Analysis Tasks. It is
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APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK 
PROBLEM

APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK PROBLEM

The average satisfaction level and the minimum number of standard deviations for each variable were obtained. The range was a minimum score of 3 points and a maximum of 21 points, respectively on the scale from 1 point to 7 points. The average level of satisfaction in life was 13.40 in middle school, 14.88 in college, and 14.08 in household income, which is lower than that of 4 million won. Subjective health was very poor at 10.00 points and very good at 17.33 points. The higher the educational background, the higher was the household income, and the better the subjective health, the better was the satisfaction of life. At p < 0.5 level, the significance of household satisfaction means sex (t = 50.82, p <. 001), age (F = 2.16 p = 0.051), and occupation = . Among them, statistically significant factors related to life satisfaction were sex, age, household income, and
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APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK 
PROBLEM

APPLICATION OF FITNESS SWITCHING GENETIC ALGORITHM FOR SOLVING 0 1 KNAPSACK PROBLEM

With the development of internet, it has been possible to obtain a variety of information from web. The amount of data on the Web is growing at 40% per year, which is expected to increase from 4.4 ZB (zettabytes) in 2013 to 44.4 ZB (zettabytes) in 2020 [1].There is a lot of information on the web, of which daily news is also large. In the past, most people received news on TV or newspapers, but now they are gaining such information from the wired Internet web or smartphone mobile apps. The era of seeing news in newspapers has been a long time ago, and it has only just begun to look at Internet news on personal computers, but now more people are getting the latest information via mobile. The way of contacting news is adapting quickly to the age according to the development of mobile device and communication technology, but the quality of news is not higher than before. In Korea, unlike other countries, most of the news is searched on the main
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