9,566 research outputs found
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming
Autonomously training interpretable control strategies, called policies,
using pre-existing plant trajectory data is of great interest in industrial
applications. Fuzzy controllers have been used in industry for decades as
interpretable and efficient system controllers. In this study, we introduce a
fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning
(FGPRL) that can select the relevant state features, determine the size of the
required fuzzy rule set, and automatically adjust all the controller parameters
simultaneously. Each GP individual's fitness is computed using model-based
batch reinforcement learning (RL), which first trains a model using available
system samples and subsequently performs Monte Carlo rollouts to predict each
policy candidate's performance. We compare FGPRL to an extended version of a
related method called fuzzy particle swarm reinforcement learning (FPSRL),
which uses swarm intelligence to tune the fuzzy policy parameters. Experiments
using an industrial benchmark show that FGPRL is able to autonomously learn
interpretable fuzzy policies with high control performance.Comment: Accepted at Genetic and Evolutionary Computation Conference 2018
(GECCO '18
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
Kajian terhadap ketahanan hentaman ke atas konkrit berbusa yang diperkuat dengan serat kelapa sawit
Konkrit berbusa merupakan sejenis konkrit ringan yang mempunyai kebolehkerjaan yang
baik dan tidak memerlukan pengetaran untuk proses pemadatan. Umum mengenali
konkrit berbusa sebagai bahan binaan yang mempunyai sifat kekuatan yang rendah dan
lemah terutama apabila bahan binaan ini dikenakan tenaga hentaman yang tinggi.
Namun begitu, konkrit berbusa merupakan bahan yang berpotensi untuk dijadikan
sebagai bahan binaan yang berkonsepkan futuristik. Binaan futuristik adalah binaan yang
bercirikan ringan, ekonomi, mudah dari segi kerja pembinaan dan yang paling penting
adalah mesra alam. Dalam kajian ini, konkrit berbusa ditambah serat buangan pokok
kelapa sawit untuk untuk meningkatkan sifat kekuatan atau rapuh. Serat kelapa sawit juga
berfungsi mempertingkatkan ketahanan hentaman terutamanya aspek nilai penyerapan
tenaga hentaman dan nilai tenaga hentaman. Kandungan peratusan serat kelapa sawit
yang digunakan adalah 10%, 20% dan 30% dengan dua ketumpatan konkrit berbusa iaitu
1000kg/m3
dan 1400kg/m3
. Untuk menentukan nilai penyerapan tenaga hentaman dan
nilai tenaga hentaman, ujikaji Indentasi dan ujikaji hentaman dilakukan ke atas sampel�sampel yang telah diawet selama 28 hari. Luas bawah graf tegasan-terikan yang
diperolehi daripada ujikaji Indentasi merupakan nilai penyerapan tenaga hentaman bagi
sampel konkrit berbusa. Untuk ujikaji hentaman, keputusan ujikaji dinilai berdasarkan
nilai tenaga hentaman untuk meretakkan sampel yang diperolehi daripada mesin ujikaji
dynatup. Secara keseluruhannya, hasil dapatan utama bagi kedua-dua ujikaji
menunjukkan sampel yang mengandungi peratusan serat kelapa sawit sebanyak 20%
mempunyai nilai penyerapan tenaga hentaman dan nilai tenaga hentaman yang tinggi.
Serapan tenaga maksimum adalah sebanyak 4.517MJ/m3
untuk ketumpatan 1400kg/m3
.
Ini menunjukkan ketumpatan 1400kg/m3
berupaya menyerap tenaga lebih baik
berbanding ketumpatan 1000kg/m3
. Manakala untuk nilai tenaga hentaman maksimum
adalah sebanyak 27.229J untuk ketumpatan 1400kg/m3
. Hasil dapatan tersebut menunjukkan ketumpatan 1400kg/m3
dengan peratusan serat sebanyak 20% berupaya
mengalas tenaga hentaman yang lebih banyak sebelum sampel retak. Kesimpulannya,
peningkatan ketumpatan konkrit berbusa dan pertambahan serat buangan kelapa sawit ke
dalam konkrit berbusa dapat meningkatkan ciri ketahanan hentaman konkrit berbusa
khususnya aspek nilai penyerapan tenaga hentaman dan nilai tenaga hentaman
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
A Comparison of Nature Inspired Algorithms for Multi-threshold Image Segmentation
In the field of image analysis, segmentation is one of the most important
preprocessing steps. One way to achieve segmentation is by mean of threshold
selection, where each pixel that belongs to a determined class islabeled
according to the selected threshold, giving as a result pixel groups that share
visual characteristics in the image. Several methods have been proposed in
order to solve threshold selectionproblems; in this work, it is used the method
based on the mixture of Gaussian functions to approximate the 1D histogram of a
gray level image and whose parameters are calculated using three nature
inspired algorithms (Particle Swarm Optimization, Artificial Bee Colony
Optimization and Differential Evolution). Each Gaussian function approximates
thehistogram, representing a pixel class and therefore a threshold point.
Experimental results are shown, comparing in quantitative and qualitative
fashion as well as the main advantages and drawbacks of each algorithm, applied
to multi-threshold problem.Comment: 16 pages, this is a draft of the final version of the article sent to
the Journa
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