Proceedings ArticleDOI
Genetic Algorithm- A Literature Review
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TLDR
How a genetic algorithm work and what are the process is included in this is also discussed and the features and application of genetic algorithm are mentioned in the paper.Abstract:
Genetic Algorithm (GA) may be attributed as method for optimizing the search tool for difficult problems based on genetics selection principle. In additions to Optimization it also serves the purpose of machine learning and for Research and development. It is analogous to biology for chromosome generation with variables such as selection, crossover and mutation together constituting genetic operations which would be applicable on a random population initially. GA aims to yield solutions for the consecutive generations. The extent of success in individual production is directly in proportion to fitness of solution which is represented by it, thereby ensuring that quality in successive generations will be better. The process is concluded once an GA is most suitable for the issues that need optimization associated with some computable system.. John Holland may be regarded as funding father of original genetic algorithm and is attributed to year 1970’s as funding date. Additionally a random search method represented by Charles Darwin for a defined search space in order to effetely solve a problem. In this paper, what is genetic algorithm and its basic workflow is discussed how a genetic algorithm work and what are the process is included in this is also discussed. Further, the features and application of genetic algorithm are mentioned in the paper.read more
Citations
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Making sense of big data.
TL;DR: As data sets grow in size, new computational tools are needed, and Jeffrey Perkel looks at the ins and outs of data analysis.
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A comparative analysis of K-Nearest Neighbour, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning
TL;DR: In this article , the authors reviewed and compared five machine learning algorithms, namely, KNN, GA, SVM, Decision Tree (DT), Long Short Term Memory (LSTM) and Support Vector Machine (SVM).
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A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities
TL;DR: This paper is focused on the concise review of the specific applications of genetic algorithms in forecasting commodity prices, and combines three important—yet not often discussed—topics: genetic algorithms, their hybrids with other tools, and commodity prices issues.
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A new hybrid algorithm for path planning of mobile robot
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References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Journal ArticleDOI
Cleanroom Software Engineering
TL;DR: The Cleanroom process gives management an engineering approach to release reliable products that can be engineered under statistical quality control and delivered with better quality.
Journal ArticleDOI
Making Sense of Big Data.
TL;DR: This article espouses a very diff erent position: that MapReduce is “good enough,” and that instead of trying to invent screwdrivers, the authors should simply get rid of everything that’s not a nail.
Book ChapterDOI
On the mean convergence time of multi-parent genetic algorithms without selection
TL;DR: An exact model based on Markov chains is proposed to formulate the variation of gene frequency and reveals the pairwise equivalence phenomenon in the number of parents and indicates the acceleration of genetic drift in MPGAs.