Listar por tema "Evolutionary computation"
Mostrando ítems 1-9 de 9
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A New Evolutionary Computation Technique for 2D: Morphogenesis and Information Processing
(WSEAS Press, 2007-03)[Abstract] Paper presents a new model that takes the behaviour of biological cells and tries to adapt some of their characteristics to the artificial cells in order to solve computational problems. This model can be related ... -
Business failure prediction models with high and stable predictive power over time using genetic programming
(Springer, 2024)[Abstract]: This study focuses on the deterioration of the predictive power and the analysis of the predictive stability of business failure prediction models, an aspect not sufficiently analysed in previous research. ... -
Efficiency of Specialized Genetic Operators in Non-dominated Tournament Genetic Algorithm (NTGA2) Applied to Multi-objective Multi-skill Resource Constrained Project Scheduling Problem
(Springer, 2024-09-09)[Abstract] The Multi-Objective Multi-Skill Resource Constrained Project Scheduling Problem (MS-RCPSP) is an NP-hard real-world problem that can be solved by metaheuristics like the Non-Dominated Tournament Genetic Algorithm ... -
Evolutionary Computation and QSAR Research
(Bentham Science, 2013)[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly ... -
Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in Schizophrenia
(Molecular Diversity Preservation International, 2010)[Abstract] Single nucleotide polymorphisms (SNPs) can be used as inputs in disease computational studies such as pattern searching and classification models. Schizophrenia is an example of a complex disease with an important ... -
Population Subset Selection for the Use of a Validation Dataset for Overfitting Control in Genetic Programming
(Taylor & Francis Group, 2019-07-31)[Abstract] Genetic Programming (GP) is a technique which is able to solve different problems through the evolution of mathematical expressions. However, in order to be applied, its tendency to overfit the data is one of ... -
Predicting vertical urban growth using genetic evolutionary algorithms in Tokyo’s minato ward
(American Society of Civil Engineers, 2018-03)[Abstract] This article explores the use of evolutionary genetic algorithms to predict scenarios of urban vertical growth in large urban centers. Tokyo’s Minato Ward is used as a case study because it has been one of the ... -
Self-tuning of disk input–output in operating systems
(Elsevier Inc., 2012-01)One of the most difficult and hard to learn tasks in computer system management is tuning the kernel parameters in order to get the maximum performance. Traditionally, this tuning has been set using either fixed configurations ... -
Variable selection in the prediction of business failure using genetic programming
(Elsevier B.V., 2024-04-08)This study focuses on dimensionality reduction by variable selection in business failure prediction models. A new method of dimensionality reduction by variable selection using Genetic Programming is proposed, which takes ...