Ritu Verma, Sunita Dhingra · 2011
Multiprocessor task scheduling (MPTS) is an important and computationally difficult problem. Multiprocessors have emerged as a powerful computing means for running real-time applications especially due to limitation of uni-processor syste m for not having sufficient enough capability to execute all the tas ks. This paper describes multiprocessor task scheduling in the for m of permutation flow shop scheduling, which has an objective function for minimizing the makespan. Here, we will conclude how the performance of genetic algorithms (value of the makespan of the schedule) varies with the variation of Genet ic Algorithm (GA) control parameters (population size, crossover probability and mutation probability).
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Abhay Bairagi · Zenodo (CERN European Organization for Nuclear Research) · 2024
A novel genetic algorithm based decisive approach for detection of influencing node in terrorist network (An Anti-Terrorism Approach) Saurabh Singh 1, Anu Sayal2, Om Prakash Chauhan1, Mahesh Motwani3, Divyani Indurkhya1, Khushi Koshta1, Abhay Bairagi1 1Department of computer science and engineering, Jabalpur engineering college, Jabalpur, Madhya Pradesh, India. 2Taylor's University, Subang Jaya, Selangor, Malaysia 3Department of computer science and engineering, Rajiv Gandhi Prodhyogiki Vishwavidyalaya Bhopal, Bhopal, Madhya Pradesh, India.
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Maamar Ali Saud Al Tobi, Geraint Bevan · 2022
Genetic algorithm (GA) has been found to be a vital method when integrated with the AI methods and particularly for the optimization and feature selections. Since chapter 3 explored the AI and some of its common methods, this chapter aims to continue with such essential tool like (GA). GA was introduced by John Holland (1975). It uses the Darwinian's concept of survival fitness, to tackle the required problem by producing individuals that are better. This would lead to various solutions that are possible to match and compete with each other. It is basically an optimisation that is applied to complex functions. It is noteworthy to know that GA is similar to chromosomes in individuals that are represented by linear string. This chapter gives an overview of GA and its application in Artificial Intelligence (AI).
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Anupam Shukla, Ritu Tiwari, Rahul Kala · Studies in computational intelligence · 2010
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Wolfgang Karl Härdle · 2022
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Claudia Lawrenz, Frank Westerhoff · 2011
Motivated by empirical evidence, we construct a model where heterogeneous boundedly rational market participants rely on a mix of technical and fundamental trading rules. The rules are applied according to a weighting scheme. Traders evaluate and update their mix of rules by a genetic algorithm. Already for a low probability of fundamental shocks the interaction between the traders results in a complex behavior of the exchange rates. Simulations of the model produce a high volatility, unit roots of the exchange rate, a fuzzy relationship between news and exchange rate movements, cointegration between the exchange rate and its fundamental, fat tails of returns, a declining kurtosis under time aggregation, and evidence of mean reversion and of cluster in both volatility and trading volume.
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A. Baghbanpourasla, G. Najarbashi B C, M. Seyedkazemi · 2008
In this paper based on the notion of entanglement witness, a new measure of entanglement called floating entanglement witness measure is introduced which satisfies some of the usual properties of a good entanglement measure. By exploiting genetic algorithm, we introduce a classical algorithm that computes floating entanglement witness measure. This algorithm also provides a method for finding entanglement witness for a given entangled state.
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Chun‐Wei Tsai, Ming‐Chao Chiang · Elsevier eBooks · 2023
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Meysam Kalantari, Ahad Kazemi, Mohammad Saleh Zakerinia et al. · 2012
Abstract � The optimal sizing and placement of distributed generation units (DGs) are becoming very attractive to researchers these days. In this paper, a genetic algorithm approach has been used for allocating DGs in distribution systems with mixed load model which includes residential loads, commercial loads, industrial loads and constant loads. In this method the multiobjective minimization is proposed using genetic algorithm. Power losses (active and reactive) and improvement of voltage profile are minimized regarding to system constraints (Power equality, line capacity limits and voltage drop limits). Power flow has been done by Backward-Forward Sweep method. The proposed model has been applied to 37-bus distribution system. 1.
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Tayaranian Hosseini, P.T., Darabad, M.R. · ePrints Soton (University of Southampton) · 2008
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Modestus O. Okwu, Lagouge K. Tartibu · Studies in computational intelligence · 2020
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2025
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K. C. Giannakoglou · 2016
The Society shall not be responsible for statements or opinions advanced in papers or discussion at meetings of the Society or of its Divisions or Sections, or printed In Its publications. Discussion Is printed only If the paper Is published In an ASME Journal. Authorization to photocopy for Internal or personal use is granted to libraries and other users registered with the Copyright Clearance Center (CCC) provided
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Babak Zolghadr‐Asli · 2023
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Adarsh Subash · Open MIND · 2026
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2008
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Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho et al. · 2013
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2013
Abstract. In this paper we analyze the image compression problem using genetic clustering algorithms based on the pixels of the image. The main problem to solve is to find an algorithm that performs this clustering efficiently. Nowadays the possibility of solving clustering problems with genetic algorithms is being studied. In this paper we make use of genetic algorithms to obtain an ordered representation of the image and then we perform the clustering to obtain the compression. With this purpose in mind, we have developed different clustering methods to apply to the ordered representation.
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Unal, Muhammet, Ak, Ayca · Dspace Repository (Marmara Üniversitesi) · 2020
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Unal, Muhammet, Ak, Ayca · Dspace Repository (Marmara Üniversitesi) · 2013
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