Results 261 to 270 of about 199,526 (313)

Quantum genetic selection [PDF]

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

Some of the next articles are maybe not open access.

Related searches:

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   +3 more sources

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.
Ting Kuo, Shu-Yuen Hwang
openaire   +3 more sources

Genetic algorithms in feature selection

IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028), 2003
We use a genetic algorithm (GA) for the feature selection problem. The method explores the space of possible subsets to obtain the set of features that maximizes the predictive accuracy and minimizes irrelevant attributes. We introduce a multiple correlation in a fitness function used by the GA to evaluate the fitness of each feature subset regarding ...
Yulu Qi, N. Chaikla
openaire   +2 more sources

On the effect of selection in genetic algorithms

Random Structures and Algorithms, 2001
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
openaire   +2 more sources

A genetic algorithm for graphical model selection

Journal of the Italian Statistical Society, 1998
Graphical 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
openaire   +4 more sources

Genetic algorithms as a strategy for feature selection

Journal of Chemometrics, 1992
AbstractGenetic 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, BOGGIA, RAFFAELLA, M. TERRILE   +2 more
openaire   +3 more sources

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, M. E. Ingrim, Susan E. Carlson   +2 more
openaire   +1 more source

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.
Rene Schulz, Adelinde M. Uhrmacher, Roland Ewald   +2 more
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