Journal•ISSN: 0020-7160
International Journal of Computer Mathematics
About: International Journal of Computer Mathematics is an academic journal. The journal publishes majorly in the area(s): Iterative method & Nonlinear system. It has an ISSN identifier of 0020-7160. Over the lifetime, 5157 publication(s) have been published receiving 49232 citation(s).
Topics: Iterative method, Nonlinear system, Boundary value problem, Numerical analysis, Partial differential equation
Papers published on a yearly basis
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TL;DR: In this article, three approaches to the quantitative definition of information are presented: information-based, information-aware and information-neutral approaches to quantifying information in the context of information retrieval.
Abstract: (1968). Three approaches to the quantitative definition of information. International Journal of Computer Mathematics: Vol. 2, No. 1-4, pp. 157-168.
2,533 citations
TL;DR: A method of generating non-linear transformations for increasing the rate and expanding the domain of convergence of sequences is presented, which represent in a certain sense a generalization of the well-known transformations due to Shanks and are more efficient.
Abstract: A method of generating non-linear transformations for increasing the rate and expanding the domain of convergence of sequences is presented. These transformations represent in a certain sense a generalization of the well-known transformations due to Shanks, and in many cases are more efficient. The transformations would seem to have important application in computing results from formal solutions to problems in applied mathematics when these solutions are obtained in the form of series or sequences having poor convergence. An indication is also given of application to the“evaluation”of divergent formal solutions.
365 citations
TL;DR: Experimental results on the major benchmarking functions used for performance evaluation of Genetic Algorithms (GAs) are presented, including the effect of population size, crossover probability, mutation rate and pseudorandom generator.
Abstract: This paper presents experimental results on the major benchmarking functions used for performance evaluation of Genetic Algorithms (GAs). Parameters considered include the effect of population size, crossover probability, mutation rate and pseudorandom generator. The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated.
238 citations
TL;DR: This paper introduces a four point explicit decoupled group (EDG) iterative method as a new Poisson solver and is shown to be very much faster compared to existing explicit group (EG) methods.
Abstract: The aim of this paper is to introduce a four point explicit decoupled group (EDG) iterative method as a new Poisson solver. The method is shown to be very much faster compared to existing explicit group (EG) methods due to D. J. Evans and M. J. Biggins (1982) and W. Yousif and D. J. Evans (1985). Some numerical experiments are included to confirm our recommendation.
201 citations
TL;DR: The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State replacement Model (SSRM) is evaluated.
Abstract: This paper presents a review and experimental results on the major benchmarking functions used for performance control of Genetic Algorithms (GAs). Parameters considered include the effect of population size, crossover probability and pseudo-random number generators (PNGs). The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated.
189 citations