Results 291 to 300 of about 586,435 (341)

A genetic algorithm with disruptive selection

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1996
Genetic 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.
T, Kuo, S Y, Hwang
openaire   +2 more sources

Genetic Algorithm Guided Selection:  Variable Selection and Subset Selection

Journal of Chemical Information and Computer Sciences, 2002
A 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
openaire   +2 more sources

Selecting Simulation Algorithm Portfolios by Genetic Algorithms

2010 IEEE Workshop on Principles of Advanced and Distributed Simulation, 2010
An 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, Rene Schulz, Adelinde M. Uhrmacher   +2 more
openaire   +1 more source

Genetic Operators Impact on Genetic Algorithms Based Variable Selection

2020
This paper faces the problem of variables selection through the use of a genetic algorithm based metaheuristic approach. The method is based on the evolution of a population of variables subsets, which is led by the genetic operators determining their selection and improvement through the algorithm generations. The impact of different genetic operators
Vannucci M., Colla V., Cateni S.
openaire   +1 more source

Feature Selection using Genetic Algorithm

Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, 2016
Genetic algorithms (GAs) have been used for feature selection with binary representation. Even if binary representation has perfect probability to include or remove a feature in the search process, some works in the field of chemometrics have reported criticism about a high number of features selected by GA implementations.
Lauro C.M. de Paula, Anderson S. Soares, Telma Woerle de Lima, Clarimar J. Coelho   +3 more
openaire   +1 more source

Sexual Selection for Genetic Algorithms

Artificial Intelligence Review, 2003
Genetic 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 ...
GOH, Kai Song, LIM, Andrew, Rodrigues, Brian   +2 more
openaire   +2 more sources

Component Selection Using Genetic Algorithms

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.
Susan E. Carlson, Michael Ingrim, Ronald Shonkwiler   +2 more
openaire   +1 more source

Quantum genetic selection

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021
This paper proposes an innovative selection operator based on concepts from quantum mechanics. In particular, a quantum state is used to embody genetic individuals and their fitness values, and a quantum algorithm known as amplitude amplification is used to modify this state in order to create a quantum superposition in which the probability to measure
Acampora G., Schiattarella R., Vitiello A.   +2 more
openaire   +2 more sources

Vendor selection using genetic algorithm

The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems, 2012
Selecting 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, M. Ameer Ali, Shamim Ripon, Md. Humayun Kabir, Nikhil Chandra Shil   +4 more
openaire   +1 more source

Genetic Algorithms and Model Selection

2017
With the rise of competition in the market, enhancing the marketing strategies has become the main interest of business owners to increase their revenues. And in order to achieve that, the main factor is to have science-based tactics like the ones provided by MassTer, the MMM software that uses advanced algorithms to estimate marketing mix models such ...
Ayari, Amani, Sayadi, Mounir
openaire   +1 more source