Results 211 to 220 of about 228,099 (265)
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A genetic algorithm with disruptive selection
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1996Genetic algorithms are a class of adaptive search techniques based on the principles of population genetics. The metaphor underlying genetic algorithms is that of natural evolution. Applying the "survival-of-the-fittest" principle, traditional genetic algorithms allocate more trials to above-average schemata.
Ting Kuo, Shu-Yuen Hwang
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Sexual Selection for Genetic Algorithms
Artificial Intelligence Review, 2003Genetic Algorithms (GA) have been widely used in operations research and optimization since first proposed. A typical GA comprises three stages, the encoding, the selection and the recombination stages. In this work, we focus our attention on the selection stage of GA, and review a few commonly employed selection schemes and their associated scaling ...
Kai Song Goh +2 more
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On the effect of selection in genetic algorithms
Random Structures and Algorithms, 2001Summary: To study the effect of selection with respect to mutation and mating in genetic algorithms, we consider two simplified examples in the infinite population limit. Both algorithms are modeled as measure valued dynamical systems and are designed to maximize a linear fitness on the half line. Thus, they both trivially converge to infinity.
Christian Mazza, Didier Piau
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Selecting Simulation Algorithm Portfolios by Genetic Algorithms
2010 IEEE Workshop on Principles of Advanced and Distributed Simulation, 2010An algorithm portfolio is a set of algorithms that are bundled together for increased overall performance. While being mostly applied to computationally hard problems so far, we investigate portfolio selection for simulation algorithms and focus on their application to adaptive simulation replication.
Roland Ewald +2 more
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Algorithm Selection on Adaptive Operator Selection: A Case Study on Genetic Algorithms
2021The present study applies Algorithm Selection (AS) to Adaptive Operator Selection (AOS) for further improving the performance of the AOS methods. AOS aims at delivering high performance in solving a given problem through combining the strengths of multiple operators.
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Probabilistic selection in cellular genetic algorithm
2012 8th International Conference on Natural Computation, 2012In this paper, we introduce a new selection operator, namely, a Probabilistic Selection operator which allows us to control the selection pressure in cellular genetic algorithms through reducing the effective neighborhood radius. One advantage for having probabilistic selection is that, once we have our probability density function in hand, we can ...
Hann-Huei Foong +2 more
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A Genetic Algorithm for the Index Selection Problem
2003This paper considers the problem of minimizing the response time for a given database workload by a proper choice of indexes. This problem is NP-hard and known in the literature as the Index Selection Problem (ISP). We propose a genetic algorithm (GA) for solving the ISP.
Jozef Kratica, Ivana Ljubic, Dusan Tosic
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Sensor Selection Optimization with Genetic Algorithms
2019 IEEE SENSORS, 2019Sensor networks and systems may incorporate sensors and sensor platforms of various quality and security. Current methods of sensor selection fail to produce effective and efficient decisions and to scale up to real-life cases. This paper develops and describes an intelligent optimization technique based on the genetic algorithms, whose execution time ...
Igor Khokhlov +2 more
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Gene Selection Using Genetic Algorithms
2004Microarrays are emerging technologies that allow biologists to better understand the interactions between disease and normal states, at genes level. However, the amount of data generated by these tools becomes problematic when data are supposed to be automatically analyzed (e.g., for diagnostic purposes).
Bruno Feres de Souza +1 more
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Genetic algorithms in feature and instance selection
Knowledge-Based Systems, 2013Feature selection and instance selection are two important data preprocessing steps in data mining, where the former is aimed at removing some irrelevant and/or redundant features from a given dataset and the latter at discarding the faulty data. Genetic algorithms have been widely used for these tasks in related studies.
Chih-Fong Tsai +2 more
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