Topic
Soft computing
About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.
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01 Mar 2008
TL;DR: This paper mathematically and experimentally proves that the simultaneous consideration of randomness and opposition is more advantageous than pure randomness, and applies that to accelerate differential evolution (DE).
Abstract: For many soft computing methods, we need to generate random numbers to use either as initial estimates or during the learning and search process. Recently, results for evolutionary algorithms, reinforcement learning and neural networks have been reported which indicate that the simultaneous consideration of randomness and opposition is more advantageous than pure randomness. This new scheme, called opposition-based learning, has the apparent effect of accelerating soft computing algorithms. This paper mathematically and also experimentally proves this advantage and, as an application, applies that to accelerate differential evolution (DE). By taking advantage of random numbers and their opposites, the optimization, search or learning process in many soft computing techniques can be accelerated when there is no a priori knowledge about the solution. The mathematical proofs and the results of conducted experiments confirm each other.
303 citations
Book•
08 Dec 1999
291 citations
Book•
01 Mar 1993TL;DR: The world of scientific computing linear algebra parallel and vector computing polynomial approximation continuous problems solved discretely direct solution of linear equations parallel direct methods relaxation-type iterative methods conjugate gradient-type methods.
Abstract: The world of scientific computing linear algebra parallel and vector computing polynomial approximation continuous problems solved discretely direct solution of linear equations parallel direct methods relaxation-type iterative methods conjugate gradient-type methods.
288 citations
TL;DR: An overview of some soft computing techniques as well as their applications in underground excavations is presented and a case study is adopted to compare the predictive performances ofsoft computing techniques including eXtreme Gradient Boosting, Multivariate Adaptive Regression Splines, and Support Vector Machine in estimating the maximum lateral wall deflection induced by braced excavation.
Abstract: Soft computing techniques are becoming even more popular and particularly amenable to model the complex behaviors of most geotechnical engineering systems since they have demonstrated superior predictive capacity, compared to the traditional methods. This paper presents an overview of some soft computing techniques as well as their applications in underground excavations. A case study is adopted to compare the predictive performances of soft computing techniques including eXtreme Gradient Boosting (XGBoost), Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), and Support Vector Machine (SVM) in estimating the maximum lateral wall deflection induced by braced excavation. This study also discusses the merits and the limitations of some soft computing techniques, compared with the conventional approaches available.
287 citations