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On the effect of selection in genetic algorithms
Random Structures and Algorithms, 2001To 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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A genetic algorithm for graphical model selection
Journal of the Italian Statistical Society, 1998Graphical log-linear model search is usually performed by using stepwise procedures in which edges are sequentially added or eliminated from the independence graph. In this paper we implement the search procedure as a genetic algorithm and propose a crossover operator which operates on subgraphs. In a simulation study the proposed procedure is shown to
Poli I, Roverato A
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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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Component Selection Using Genetic Algorithms [PDF]
19th Design Automation Conference: Volume 1 — Mechanical System Dynamics; Concurrent and Robust Design; Design for Assembly and Manufacture; Genetic Algorithms in Design and Structural Optimization, 1993 Abstract Genetic algorithms are investigated for use in obtaining optimal component configurations in dynamic engineering systems. Given a system layout, a database of component information from manufacturers’ catalogs, and a design specification, genetic algorithms are used to successfully select an optimal set of components.
Ronald W. Shonkwiler +2 more
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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.
Rene Schulz +2 more
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Genetic Algorithm Guided Selection: Variable Selection and Subset Selection
Journal of Chemical Information and Computer Sciences, 2002A novel Genetic Algorithm guided Selection method, GAS, has been described. The method utilizes a simple encoding scheme which can represent both compounds and variables used to construct a QSAR/QSPR model. A genetic algorithm is then utilized to simultaneously optimize the encoded variables that include both descriptors and compound subsets.
Sung Jin Cho, Mark A. Hermsmeier
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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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Entropy-Boltzmann selection in the genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2003A new selection method, entropy-Boltzmann selection, for genetic algorithms (GAs) is proposed. This selection method is based on entropy and importance sampling methods in Monte Carlo simulation. It naturally leads to adaptive fitness in which the fitness function does not stay fixed but varies with the environment.
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Genetic algorithms in feature and instance selection [PDF]
Knowledge-Based Systems, 2013 Feature 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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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 ...
Leon Reznik +2 more
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