Bus Dwell Time Modeling Using Gene Expression Programming
01 Jun 2015-Computer-aided Civil and Infrastructure Engineering (John Wiley & Sons, Ltd)-Vol. 30, Iss: 6, pp 478-489
TL;DR: A gene expression programming (GEP)-based approach that shows prospects how to estimate bus dwell time (BDT) more accurately and overcome some of the issues associated with the multiple linear regression (MLR) method is proposed in this article.
Abstract: A gene expression programming (GEP)-based approach that shows prospects how to estimate bus dwell time (BDT) more accurately and overcome some of the issues associated with the multiple linear regression (MLR) method is proposed in this article. The model is calibrated and validated using the data collected from 22 bus stops in Auckland and compared against the MLR model based on five different performance measures: mean error, mean absolute error, root mean square error, mean absolute percentage error, and R² value. The restrictions to stick with a predefined model and the need to satisfy assumptions made on multicollinearity, homoscedasticity, and the normality of random error are often difficult to satisfy.
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212 citations
TL;DR: A surrogate-assisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately is proposed.
Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogate-assisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of many-objective optimization test problems.
209 citations
Cites background from "Bus Dwell Time Modeling Using Gene ..."
...namic design optimization [22], drug design [23], or flowshop scheduling problems in [24]....
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...Take a ten-job and five-machine flowshop scheduling problem as an example [24], it will take...
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TL;DR: This paper presents a comprehensive review on the recent progress of GEP, with enhanced designs from six aspects, i.e., encoding design, evolutionary mechanism design, adaptation design, cooperative co-evolutionary design, constant creation design, and parallel design.
Abstract: Gene Expression Programming (GEP) is a popular and established evolutionary algorithm for automatic generation of computer programs. In recent decades, GEP has undergone rapid advancements and developments. A number of enhanced GEPs have been proposed to date and the real world applications that use them are also multiplying fast. In view of the steadfast growth of GEP and its importance to both the academia and industry, here a review on GEP is considered. In particular, this paper presents a comprehensive review on the recent progress of GEP. The state-of-the-art approaches of GEP, with enhanced designs from six aspects, i.e., encoding design, evolutionary mechanism design, adaptation design, cooperative co-evolutionary design, constant creation design, and parallel design, are presented. The theoretical studies and intriguing representative applications of GEP are given. Finally, a discussion of potential future research directions of GEP is also provided.
83 citations
TL;DR: A review of research reported on simulated annealing (SA) finds different cooling/annealing schedules are summarized and recent applications of SA in engineering are reviewed.
Abstract: This paper presents a review of research reported on simulated annealing (SA) Different cooling/annealing schedules are summarized Variants of SA are delineated Recent applications of SA in engineering are reviewed
75 citations
TL;DR: The proposed algorithm, named Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (CMA-PAES-HAGA), overcomes the limitation of CMA- PAES in handling more than two objectives and displays a remarkably good performance on a scalable test suite in five, seven, and ten-objective problems.
Abstract: Real-world problems often involve the optimisation of multiple conflicting objectives. These problems, referred to as multi-objective
optimisation problems, are especially challenging when more than three objectives are considered simultaneously.
This paper proposes an algorithm to address this class of problems. The proposed algorithm is an evolutionary algorithm based on an evolution
strategy framework, and more specifically, on the Covariance Matrix Adaptation Pareto Archived Evolution Strategy (CMA-PAES). A novel
selection mechanism is introduced and integrated within the framework. This selection mechanism makes use of an adaptive grid to perform a
local approximation of the hypervolume indicator which is then used as a selection criterion. The proposed implementation, named Covariance
Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (CMA-PAES-HAGA), overcomes the
limitation of CMA-PAES in handling more than two objectives and displays a remarkably good performance on a scalable test suite in five, seven,
and ten-objective problems. The performance of CMA-PAES-HAGA has been compared with that of a competition winning meta-heuristic,
representing the state-of-the-art in this sub-field of multi-objective optimisation.
The proposed algorithm has been tested in a seven-objective real-world application, i.e. the design of an aircraft lateral control system. In this
optimisation problem, CMA-PAES-HAGA greatly outperformed its competitors.
69 citations
Cites methods from "Bus Dwell Time Modeling Using Gene ..."
...Engineering modelling [69, 75, 84] as well as parameter identification [5] are optimisation problems....
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References
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Proceedings Article•
20 Aug 1995TL;DR: The results indicate that for real-word datasets similar to the authors', the best method to use for model selection is ten fold stratified cross validation even if computation power allows using more folds.
Abstract: We review accuracy estimation methods and compare the two most common methods crossvalidation and bootstrap. Recent experimental results on artificial data and theoretical re cults in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leaveone-out cross-validation. We report on a largescale experiment--over half a million runs of C4.5 and a Naive-Bayes algorithm--to estimate the effects of different parameters on these algrithms on real-world datasets. For crossvalidation we vary the number of folds and whether the folds are stratified or not, for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, The best method to use for model selection is ten fold stratified cross validation even if computation power allows using more folds.
11,185 citations
"Bus Dwell Time Modeling Using Gene ..." refers background in this paper
...The number of folds typically ranges from 10 to 20 (Kohavi, 1995)....
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TL;DR: A slightly more complex rule-of thumb is introduced that estimates minimum sample size as function of effect size as well as the number of predictors and it is argued that researchers should use methods to determine sample size that incorporate effect size.
Abstract: Numerous rules-of-thumb have been suggested for determining the minimum number of subjects required to conduct multiple regression analyses. These rules-of-thumb are evaluated by comparing their results against those based on power analyses for tests of hypotheses of multiple and partial correlations. The results did not support the use of rules-of-thumb that simply specify some constant (e.g., 100 subjects) as the minimum number of subjects or a minimum ratio of number of subjects (N) to number of predictors (m). Some support was obtained for a rule-of-thumb that N ≥ 50 + 8 m for the multiple correlation and N ≥104 + m for the partial correlation. However, the rule-of-thumb for the multiple correlation yields values too large for N when m ≥ 7, and both rules-of-thumb assume all studies have a medium-size relationship between criterion and predictors. Accordingly, a slightly more complex rule-of thumb is introduced that estimates minimum sample size as function of effect size as well as the number of predictors. It is argued that researchers should use methods to determine sample size that incorporate effect size.
3,105 citations
"Bus Dwell Time Modeling Using Gene ..." refers background in this paper
...The data sample size is well above the minimum recommended sample size as suggested by Green (1991)....
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Journal Article•
TL;DR: Gene expression programming, a genotype/phenotype genetic algorithm (linear and ramified), is presented here for the first time as a new technique for the creation of computer programs with high efficiency that greatly surpasses existing adaptive techniques.
Abstract: Gene expression programming, a genotype/phenotype genetic algorithm (linear and ramified), is presented here for the first time as a new technique for the creation of computer programs. Gene expression programming uses character linear chromosomes composed of genes structurally organized in a head and a tail. The chromosomes function as a genome and are subjected to modification by means of mutation, transposition, root transposition, gene transposition, gene recombination, and oneand two-point recombination. The chromosomes encode expression trees which are the object of selection. The creation of these separate entities (genome and expression tree) with distinct functions allows the algorithm to perform with high efficiency that greatly surpasses existing adaptive techniques. The suite of problems chosen to illustrate the power and versatility of gene expression programming includes symbolic regression, sequence induction with and without constant creation, block stacking, cellular automata rules for the density-classification problem, and two problems of boolean concept learning: the 11-multiplexer and the GP rule problem.
1,887 citations
"Bus Dwell Time Modeling Using Gene ..." refers background or methods in this paper
...Gene expression programming (GEP) is the most recent advancement in the family of evolutionary algorithms, proposed by Ferreira (2001), and has been implemented in traffic engineering (Bagula and Wang, 2005), environmental modeling (Hashmi et al., 2011), and soil mechanics (Kayadelen et al., 2009)....
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...GEP is the most recent advancement proposed by Ferreira (2001)....
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...Table 2 presents the values used for genetic operator parameters as recommended by its developer (Ferreira, 2001)....
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Book•
01 Jan 1998
TL;DR: This book presents a meta-modelling framework for genetic programming that automates the very labor-intensive and therefore time-heavy and expensive process of designing and implementing genetic algorithms.
Abstract: 1 Genetic Programming as Machine Learning 2 Genetic Programming and Biology 3 Computer Science and Mathematical Basics 4 Genetic Programming as Evolutionary Computation 5 Basic ConceptsThe Foundation 6 CrossoverThe Center of the Storm 7 Genetic Programming and Emergent Order 8 AnalysisImproving Genetic Programming with Statistics 9 Different Varieties of Genetic Programming 10 Advanced Genetic Programming 11 ImplementationMaking Genetic Programming Work 12 Applications of Genetic Programming 13 Summary and Perspectives A Printed and Recorded Resources B Information Available on the Internet C GP Software D Events
1,771 citations
"Bus Dwell Time Modeling Using Gene ..." refers background in this paper
...Banzhaf (1998) suggested that GEP-based modeling approach can be of great interest in cases where: the interrelation between different set of variables is not clear; one cannot easily assume or set a mathematical form to relate dependent and independent variables; traditional analytical tools are…...
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