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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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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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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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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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Vendor selection using genetic algorithm
The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems, 2012Selecting the right vendor is a complex business decision due to a huge number of competing vendors with a large number of complex criteria. The organization will suffer in the long run if vendors are not chosen wisely. Under multi criteria decision making, an algorithm, named VSFI, based on fuzzy clustering was proposed to select the most optimal ...
Shaila Sharmeen +4 more
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Genetic algorithms as a strategy for feature selection
Journal of Chemometrics, 1992AbstractGenetic algorithms have been created as an optimization strategy to be used especially when complex response surfaces do not allow the use of better‐known methods (simplex, experimental design techniques, etc.). This paper shows that these algorithms, conveniently modified, can also be a valuable tool in solving the feature selection problem ...
LEARDI, RICCARDO +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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