Results 11 to 20 of about 917,720 (266)
Gene expression programming approach to event selection in high energy physics [PDF]
, 2006 Gene Expression Programming is a new evolutionary algorithm that overcomes many limitations of the more established Genetic Algorithms and Genetic Programming. Its first application to high energy physics data analysis is presented.
Teodorescu, L
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Lexicase Selection at Scale [PDF]
Proceedings of the 2022 Genetic and Evolutionary Computation
Conference Companion (pp. 2054-2062), 2022 Lexicase selection is a semantic-aware parent selection method, which assesses individual test cases in a randomly-shuffled data stream. It has demonstrated success in multiple research areas including genetic programming, genetic algorithms, and more recently symbolic regression and deep learning.
arxiv +1 more source
Multi-objective genetic optimisation for self-organising fuzzy logic control [PDF]
, 1998 This is the post-print version of the article. The official published version can be accessed from the link below.A multi-objective genetic algorithm is developed for the purpose of optimizing the rule-base of a Self-Organising Fuzzy Logic Control ...
Abbod, MF, Linkens, DA, Mahfouf, M
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Comparison Study for Clonal Selection Algorithm and Genetic Algorithm [PDF]
International Journal of Computer Science & Information Technology
(IJCSIT) Vol 4, No 4, August 2012 pp 107-118, 2012 Two metaheuristic algorithms namely Artificial Immune Systems (AIS) and Genetic Algorithms are classified as computational systems inspired by theoretical immunology and genetics mechanisms. In this work we examine the comparative performances of two algorithms.
arxiv +1 more source
Feature selection using genetic algorithms and probabilistic neural networks [PDF]
, 2000 Selection of input variables is a key stage in building predictive models, and an important form of data mining. As exhaustive evaluation of potential input sets using full non-linear models is impractical, it is necessary to use simple fast-evaluating ...
Hunter, Andrew
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Flexible job shop scheduling using genetic algorithm and heuristic rules
Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2016 Job shop scheduling with the availability of more than one machine to perform an operation, also known as the flexible job shop scheduling problem, is computationally NP-hard.
Parinya KAWEEGITBUNDIT, Toru EGUCHI
doaj +1 more source
A lexicographic multi-objective genetic algorithm for multi-label correlation-based feature selection [PDF]
, 2015 This paper proposes a new Lexicographic multi-objective Genetic Algorithm for Multi-Label Correlation-based Feature Selection (LexGA-ML-CFS), which is an extension of the previous single-objective Genetic Algorithm for Multi-label Correlation-based ...
Freitas, Alex A., Jungjit, Suwimol
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Rare-Event Sampling Analysis Uncovers the Fitness Landscape of the Genetic Code [PDF]
, 2022 The genetic code refers to a rule that maps 64 codons to 20 amino acids. Nearly all organisms, with few exceptions, share the same genetic code, the standard genetic code (SGC). While it remains unclear why this universal code has arisen and been maintained during evolution, it may have been preserved under selection pressure.
arxiv +1 more source
Full model selection in the space of data mining operators [PDF]
, 2012 We propose a framework and a novel algorithm for the full model selection (FMS) problem. The proposed algorithm, combining both genetic algorithms (GA) and particle swarm optimization (PSO), is named GPS (which stands for GAPSO-FMS), in which a GA is ...
Mayo, Michael +2 more
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Improving Floating Search Feature Selection using Genetic Algorithm
Journal of ICT Research and Applications, 2017 Classification, a process for predicting the class of a given input data, is one of the most fundamental tasks in data mining. Classification performance is negatively affected by noisy data and therefore selecting features relevant to the problem is a ...
Kanyanut Homsapaya, Ohm Sornil
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