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Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development [chapter]

S. J. Barrett, W. B. Langdon
2006 Advances in Intelligent and Soft Computing  
Machine learning tools, in particular support vector machines (SVM), Particle Swarm Optimisation (PSO) and Genetic Programming (GP), are increasingly used in pharmaceuticals research and development.  ...  These aspects are demonstrated via review of their current usage and future prospects in context with drug discovery activities.  ...  The authors wish to thank GSK colleagues, past and present, for their efforts in expressing the nature of their research.  ... 
doi:10.1007/978-3-540-36266-1_10 fatcat:gdq5kxmbfjfbbaccdcjpsopg4e

Hybrid feature selection methods for the Classification of Cancer in Micro-array Gene expression data: a Survey

2020 International Journal of Advanced Trends in Computer Science and Engineering  
A collection of features (genes) is one of the most successful approaches to face these challenges.  ...  But still the curse of dimensionality and the curse of sparseness are a challenge to classify the gene expression profile.  ...  Biogeography Algorithm-Hybrid Approach: Li and Yin [49] suggested the multi-objective binary biogeography (MOBBO) approach to gene selection.  ... 
doi:10.30534/ijatcse/2020/275952020 fatcat:gtag5sze3vdftaw5s4ub4olnyq

A Review of the Modification Strategies of the Nature Inspired Algorithms for Feature Selection Problem

Ruba Abu Abu Khurma, Ibrahim Aljarah, Ahmad Sharieh, Mohamed Abd Abd Elaziz, Robertas Damaševičius, Tomas Krilavičius
2022 Mathematics  
The most widely used hybridization is the one that integrates a classifier with the NIA.  ...  Hybridization is the most widely used modification technique.  ...  In [96] , the objective was to update PSO based on a clustering approach. The new GPSO uses Gaussian distribution.  ... 
doi:10.3390/math10030464 fatcat:sjg667gilzfktokxxjwdg52jbm

A Comparative Analysis of Optimization Techniques

Kanika Tyagi, Kirti Tyagi
2015 International Journal of Computer Applications  
Regression testing is an inescapable and very expensive task to be performed, often in a resource and time constrained environment.  ...  The goal is to minimize the time spent in the process of testing by reduction in the number of test cases to be used.  ...   Time and cost effective: As in CBS, code for a component can be used again and again in similar type of applications. This makes it a time and cost efficient approach.  ... 
doi:10.5120/ijca2015907399 fatcat:75x4dkcve5g5ffykqkl6uewetu

Implementing Metaheuristic Optimization Algorithms with JECoLi

Pedro Evangelista, Paulo Maia, Miguel Rocha
2009 2009 Ninth International Conference on Intelligent Systems Design and Applications  
JECoLi has been/is being used in several research projects that helped to shape its evolution, ranging application fields from Bioinformatics, to Data Mining and Computer Network optimization.  ...  The project is opensource, so JECoLi is made available under the GPL license, together with extensive documentation and examples, all included in a community Wiki-based web site (  ...  Biomarker discovery DNA microarrays allow to measure the expression of all genes in a genome and is becoming quite important in biomedical research.  ... 
doi:10.1109/isda.2009.161 dblp:conf/isda/EvangelistaMR09 fatcat:osd7yn52m5aqfebry5joltopmi

Evolutionary Computation and QSAR Research

Vanessa Aguiar-Pulido, Marcos Gestal, Maykel Cruz-Monteagudo, Juan Rabunal, Julian Dorado, Cristian Munteanu
2013 Current Computer - Aided Drug Design  
Each component (gene for GA or leaf node for PG) represents the variables or parameters involved in the problem.  ...  The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery.  ...  ACKNOWLEDGEMENTS Vanessa Aguiar-Pulido and Munteanu C.R. thank sponsorship from the "Plan I2C" and Isidro Parga Pondal research programs respectively, both funded by Xunta de Galicia (Spain) and the European  ... 
doi:10.2174/1573409911309020006 pmid:23700999 fatcat:zsipotcovzhlhg7wrgjfkc5wvu

A microarray gene expression data classification using hybrid back propagation neural network

M. Vimaladevi, B. Kalaavathi
2014 Genetika  
This technical note applies hybrid models of Back Propagation Neural networks (BPN) and fast Genetic Algorithms (GA) to estimate the feature selection in gene expression data.  ...  In cancer classification, the approach, Back propagation is sufficient and also it is a universal technique of training artificial neural networks. It is also called supervised learning method.  ...  They proposed a multi-objective Particle Swarm Optimization (PSO)-based algorithm that optimizes average node-weight and average edge-weight of the candidate subgraph simultaneously.  ... 
doi:10.2298/gensr1403013v fatcat:g2kxefiqqfedbp6of5ohhdr4wy

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Laura Calvet, Jésica de Armas, David Masip, Angel A. Juan
2017 Open Mathematics  
These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by  ...  In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual.  ...  Acknowledgement: This work has been partially supported by the Spanish Ministry of Economy and Competitiveness and FEDER (TRA2013-48180-C3-P, DPI2013-44461-P, TIN2015-66951-C2-2-R), and the Catalan Government  ... 
doi:10.1515/math-2017-0029 fatcat:ktigbziemvel3a5mpxjfz6dsra

Bio-Mimic Optimization Strategies in Wireless Sensor Networks: A Survey

Md. Adnan, Mohammd Razzaque, Ishtiaque Ahmed, Ismail Isnin
2013 Sensors  
In conventional optimization approaches, the methods need to comply with the structure of the objective function which is to be solved [2], but sometimes the derivative of the objective function cannot  ...  Obtaining QoS in these highly resource-constrained networks is not an easy task. In a number of cases, QoS metrics or parameters might even conflict with themselves.  ...  Acknowledgments Authors would like to thank Universiti Teknologi Malaysia and Ministry of Higher Education, Malaysia for sponsoring this research under vote numbers: 4D062, 07J04, and 08J35.  ... 
doi:10.3390/s140100299 pmid:24368702 pmcid:PMC3926559 fatcat:lndlgfl2ajflxhvninklnukww4

A survey on multi-objective hyperparameter optimization algorithms for Machine Learning [article]

Alejandro Morales-Hernández and Inneke Van Nieuwenhuyse and Sebastian Rojas Gonzalez
2021 arXiv   pre-print
, and approaches using a mixture of both.  ...  We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.  ...  The use of MOEA/D results in a reduction in the number of parameters to train; the comparison is not really reliable, though, as the Bayesian approach was used in a single-objective optimizer, focusing  ... 
arXiv:2111.13755v2 fatcat:q2qtofihtzev5mose5aj7odfzm

Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs

Cristian Rotaru, Octav Brudaru
2012 2012 IEEE Congress on Evolutionary Computation  
The population evolves on a bidimensional grid and is implicitly organized in geographical clusters that present a form of structural similarity between individuals.  ...  The approach systematically produces better results than the used basic genetic algorithm and better or similar results with other heuristic methods.  ...  , Using Monte Carlo Tree Search for Replanning in a Multistage Simultaneous Game Monday, Hybrid, MoH 4-3, 14:40-15:40, Computational Intelligence for Cognitive Robotics (Hybrid) 1,aoyuki Kubota, Monday  ... 
doi:10.1109/cec.2012.6256590 dblp:conf/cec/RotaruB12 fatcat:4ly3nrktw5habc6lf5err7d5py

Knowledge management overview of feature selection problem in high-dimensional financial data: cooperative co-evolution and MapReduce perspectives

A N M Bazlur Rashid, Tonmoy Choudhury
2019 Problems and Perspectives in Management  
Cooperative co-evolution, a meta-heuristic algorithm and a divide-and-conquer approach, decomposes high-dimensional problems into smaller sub-problems.  ...  The term "big data" characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs – volume, velocity, variety, and veracity - to indicate the amount of  ...  Finally, a hybrid approach of FS using PSO and ta- bu search (TS) (Shen et al., 2008) selects the genes for tumor classification using the gene expression data.  ... 
doi:10.21511/ppm.17(4).2019.28 fatcat:76yr472o6rf7vm3torvgnxfcnm

Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols

Ahmed Mahdi Jubair, Rosilah Hassan, Azana Hafizah Mohd Aman, Hasimi Sallehudin, Zeyad Ghaleb Al-Mekhlafi, Badiea Abdulkarem Mohammed, Mohammad Salih Alsaffar
2021 Applied Sciences  
The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques.  ...  To determine clustering protocols' competency, we compared the features and parameters of the clustering and examined the objectives, benefits, and key features of various clustering optimization methods  ...  Acknowledgments: The authors would like to acknowledge the support provided by the Network and Communication Technology (NCT) Research Groups, FTSM, UKM in providing facilities throughout this paper.  ... 
doi:10.3390/app112311448 fatcat:4qv3evyfonetxf626liogvihzi

Reducing Efficiency of Connectivity-Splitting Attack on Newscast via Limited Gossip [chapter]

Jakub Muszyński, Sébastien Varrette, Pascal Bouvry
2016 Lecture Notes in Computer Science  
EvoBackMusic is a multi-agent system that exploits a feed-forward neural network and a multi-objective genetic algorithm to produce background music.  ...  The ability of GP to explore a large search space in an efficient manner allows all stages of the new method to be optimised simultaneously, unlike in existing approaches.  ... 
doi:10.1007/978-3-319-31204-0_20 fatcat:27rnwllk75cv5kncys2u7utreq

A Preliminary Survey on Optimized Multiobjective Metaheuristic Methods for Data Clustering Using Evolutionary Approaches

Ramachandra Rao Kurada, K Karteeka Pavan, AV Dattareya Rao
2013 International Journal of Computer Science & Information Technology (IJCSIT)  
The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective Evolutionary Approaches (MOEAs) methods.  ...  Discovery of a majority or all of the clusters (of illogical shapes) present in the data is a long-standing goal of unsupervised predictive learning problems or exploratory pattern analysis.  ...  The same parents are used to acclimate to all object parameters in the child, and again the parents are re-selected for each action ambit factors.  ... 
doi:10.5121/ijcsit.2013.5504 fatcat:4b2ye5jfqvhddf23gptdk3mh2y
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