Energy Consumption Forecasting using Semantics Based Genetic Programming with Local Search Optimizer

Published in Journal Articles

Abstract

Energy consumption forecasting (ECF) is an important policy issue in today's economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-)perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.

  1. Mauro Castelli, Leonardo Trujillo and Leonardo Vanneschi. Energy Consumption Forecasting Using Semantic-based Genetic Programming with Local Search Optimizer. Intell. Neuroscience 2015:57:57–57:57, 2015. URL, DOI BibTeX

    @article{,
    	author = "Castelli, Mauro and Trujillo, Leonardo and Vanneschi, Leonardo",
    	title = "Energy Consumption Forecasting Using Semantic-based Genetic Programming with Local Search Optimizer",
    	journal = "Intell. Neuroscience",
    	issue_date = "January 2015",
    	volume = 2015,
    	month = "",
    	year = 2015,
    	issn = "1687-5265",
    	pages = "57:57--57:57",
    	articleno = 57,
    	numpages = 1,
    	url = "http://dx.doi.org/10.1155/2015/971908",
    	doi = "10.1155/2015/971908",
    	acmid = 2810687,
    	publisher = "Hindawi Publishing Corp.",
    	address = "New York, NY, United States"
    }
    
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Prediction of energy performance of residential buildings: a genetic programming approach

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Abstract

Energy consumption has long been emphasized as an important policy issue in today's economies. In particular, the energy efficiency of residential buildings is considered a top priority of a country's energy policy. The paper proposes a genetic programming-based framework for estimating the energy performance of residential buildings. The objective is to build a model able to predict the heating load and the cooling load of residential buildings. An accurate prediction of these parameters facilitates a better control of energy consumption and, moreover, it helps choosing the energy supplier that better fits the energy needs, which is considered an important issue in the deregulated energy market. The proposed framework blends a recently developed version of genetic programming with a local search method and linear scaling. The resulting system enables us to build a model that produces an accurate estimation of both considered parameters. Extensive simulations on 768 diverse residential buildings confirm the suitability of the proposed method in predicting heating load and cooling load. In particular, the proposed method is more accurate than the existing state-of-the-art techniques.

  1. Mauro Castelli, Leonardo Trujillo, Leonardo Vanneschi and Aleš Popovič. Prediction of energy performance of residential buildings: A genetic programming approach. Energy and Buildings 102():67 - 74, 2015. URL, DOI BibTeX

    @article{,
    	title = "Prediction of energy performance of residential buildings: A genetic programming approach",
    	journal = "Energy and Buildings",
    	volume = 102,
    	number = "",
    	pages = "67 - 74",
    	year = 2015,
    	issn = "0378-7788",
    	doi = "http://dx.doi.org/10.1016/j.enbuild.2015.05.013",
    	url = "http://www.sciencedirect.com/science/article/pii/S0378778815003849",
    	author = "Mauro Castelli and Leonardo Trujillo and Leonardo Vanneschi and Aleš Popovič"
    }
    
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A comparison of predictive measures of problem difficulty for classification with Genetic Programming

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Abstract

In the field of Genetic Programming (GP) a question exists that is difficult to solve; how can problem difficulty be determined? In
this paper the overall goal is to develop predictive tools that estimate how difficult a problem is for GP to solve. Here we analyse two groups of methods. We call the first group Evolvability Indicators (EI), measures that capture how amendable the fitness landscape is to a GP search. The second are Predictors of Expected Performance (PEP), models that take as input a set of descriptive attributes of a problem and predict the expected performance of a GP system. These predictive variables are domain specific thus problems are described in the context of the problem domain. This paper compares an EI, the Negative Slope Coefficient, and a PEP model for a GP classifier. Results suggest that the EI does not correlate with the performance of GP classifiers. Conversely, the PEP models show a high correlation with GP performance. It appears that while an EI estimates the difficulty of a search, it does not necessarily capture the difficulty of the underlying problem. However, while PEP models treat GP as a computational black-box, they can produce accurate performance predictions.

  1. Yuliana Martínez, Leonardo Trujillo, Galvan Galván-López and Pierrick Legrand. A comparison of predictive measures of problem difficulty for classification with Genetic Programming. In ERA 2012. 2012. BibTeX

    @inproceedings{,
    	title = "A comparison of predictive measures of problem difficulty for classification with Genetic Programming",
    	author = "Mart\'{i}nez, Yuliana and Trujillo, Leonardo and Galv\'{a}n-L\'{o}pez, Galvan and Legrand, Pierrick",
    	booktitle = "ERA 2012",
    	year = 2012
    }
    

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Identification of epilepsy stages from ECoG using genetic programming classifiers

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Abstract

Objective: Epilepsy is a common neurological disorder, for which a great deal of research has been devoted to analyze and characterize brain activity during seizures. While this can be done by a human expert, automatic methods still lag behind. This paper analyzes neural activity captured with Electrocorticogram (ECoG), recorded through intracranial implants from Kindling model test subjects. The goal is to automatically identify the main seizure stages: Pre-Ictal, Ictal and Post-Ictal. While visually differentiating each stage can be done by an expert if the complete time-series is available, the goal here is to automatically identify the corresponding stage of short signal segments.

Methods and materials: The proposal is to pose the above task as a supervised classification problem and derive a mapping function that classifies each signal segment. Given the complexity of the signal patterns, it is difficult to a priori choose any particular classifier. Therefore, Genetic Programming (GP), a population based meta-heuristic for automatic program induction, is used to automatically search for the mapping functions. Two GP-based classifiers are used and extensively evaluated. The signals from epileptic seizures are obtained using the Kindling model of elicited epilepsy in rodent test subjects, for which a seizure was elicited and recorded on four separate days.

Results: Results show that signal segments from a single seizure can be used to derive accurate classifiers that generalize when tested on different signals from the same subject; i.e., GP can automatically produce accurate mapping functions for intra-subject classification. A large number of experiments are performed with the GP classifiers achieving good performance based on standard performance metrics. Moreover, a proof-of-concept real-world prototype is presented, where a GP classifier is transferred and hard-coded on an embedded system using a digital-to-analogue converter and a field programmable gate array, achieving a low average classification error of 14.55%, sensitivity values between 0.65 and 0.97, and specificity values between 0.86 and 0.94.

Conclusions: The proposed approach achieves good results for stage identification, particularly when compared with previous works that focus on this task. The results show that the problem of intra-class classification can be solved with a low error, and high sensitivity and specificity. Moreover, the limitations of the approach are identified and good operating configurations can be proposed based on the results.

  1. Arturo Sotelo, Enrique Guijarro, Leonardo Trujillo, Luis N Coria and Yuliana Martínez. Identification of epilepsy stages from ECoG using genetic programming classifiers. Computers in Biology and Medicine 43(11):1713 - 1723, 2013. URL, DOI BibTeX

    @article{,
    	title = "Identification of epilepsy stages from \{ECoG\} using genetic programming classifiers",
    	journal = "Computers in Biology and Medicine",
    	volume = 43,
    	number = 11,
    	pages = "1713 - 1723",
    	year = 2013,
    	doi = "http://dx.doi.org/10.1016/j.compbiomed.2013.08.016",
    	url = "http://www.sciencedirect.com/science/article/pii/S001048251300231X",
    	author = "Arturo Sotelo and Enrique Guijarro and Leonardo Trujillo and Luis N. Coria and Yuliana Mart\'{i}nez"
    }
    
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Evolving estimators of the pointwise Holder exponent with Genetic Programming

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Abstract

The regularity of a signal can be numerically expressed using Hölder exponents, which characterize the singular structures a signal contains. In particular, within the domains of image processing and image understanding, regularity-based analysis can be used to describe local image shape and appearance. However, estimating the Hölder exponent is not a trivial task, and current methods tend to be computationally slow and complex. This work presents an approach to automatically synthesize estimators of the pointwise Hölder exponent for digital images. This task is formulated as an optimization problem and Genetic Programming (GP) is used to search for operators that can approximate a traditional estimator, the oscillations method. Experimental results show that GP can generate estimators that achieve a low error and a high correlation with the ground truth estimation. Furthermore, most of the GP estimators are faster than traditional approaches, in some cases their runtime is orders of magnitude smaller. This result allowed us to implement a real-time estimation of the Hölder exponent on a live video signal, the first such implementation in current literature. Moreover, the evolved estimators are used to generate local descriptors of salient image regions, a task for which a stable and robust matching is achieved, comparable with state-of-the-art methods. In conclusion, the evolved estimators produced by GP could help expand the application domain of Hölder regularity within the fields of image analysis and signal processing.

  1. Leonardo Trujillo, Pierrick Legrand, Gustavo Olague and Jacques Lévy Véhel. Evolving estimators of the pointwise Hölder exponent with Genetic Programming. Inf. Sci. 209:61-79, 2012. BibTeX

    @article{DBLP:journals/isci/TrujilloLOV12,
    	author = "Leonardo Trujillo and Pierrick Legrand and Gustavo Olague and Jacques L{\'e}vy V{\'e}hel",
    	title = {Evolving estimators of the pointwise H{\"o}lder exponent with Genetic Programming},
    	journal = "Inf. Sci.",
    	volume = 209,
    	year = 2012,
    	pages = "61-79",
    	ee = "http://dx.doi.org/10.1016/j.ins.2012.04.043",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    

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Interest point detection through multiobjective genetic programming

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Abstract

The detection of stable and informative image points is one of the most important low-level problems in modern computer vision. This paper proposes a multiobjective genetic programming (MO-GP) approach for the automatic synthesis of operators that detect interest points. The proposal is unique for interest point detection because it poses a MO formulation of the point detection problem. The search objectives for the MO-GP search consider three properties that are widely expressed as desirable for an interest point detector, these are: (1) stability; (2) point dispersion; and (3) high information content. The results suggest that the point detection task is a MO problem, and that different operators can provide different trade-offs among the objectives. In fact, MO-GP is able to find several sets of Pareto optimal operators, whose performance is validated on standardized procedures including an extensive test with 500 images; as a result, we could say that all solutions found by the system dominate previously man-made detectors in the Pareto sense. In conclusion, the MO formulation of the interest point detection problem provides the appropriate framework for the automatic design of image operators that achieve interesting trade-offs between relevant performance criteria that are meaningful for a variety of vision tasks.

  1. Gustavo Olague and Leonardo Trujillo. Interest point detection through multiobjective genetic programming. Appl. Soft Comput. 12(8):2566-2582, 2012. BibTeX

    @article{DBLP:journals/asc/OlagueT12,
    	author = "Gustavo Olague and Leonardo Trujillo",
    	title = "Interest point detection through multiobjective genetic programming",
    	journal = "Appl. Soft Comput.",
    	volume = 12,
    	number = 8,
    	year = 2012,
    	pages = "2566-2582",
    	ee = "http://dx.doi.org/10.1016/j.asoc.2012.03.058",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    

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Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming

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Abstract

This work describes a way of designing interest point detectors using an evolutionary-computer-assisted design approach. Nowadays, feature extraction is performed through the paradigm of interest point detection due to its simplicity and robustness for practical applications such as: image matching and view-based object recognition. Genetic programming is used as the core functionality of the proposed human-computer framework that significantly augments the scope of interest point design through a computer assisted learning process. Indeed, genetic programming has produced numerous interest point operators, many with unique or unorthodox designs. The analysis of those best detectors gives us an advantage to achieve a new level of creative design that improves the perspective for human-machine innovation. In particular, we present two novel interest point detectors produced through the analysis of multiple solutions that were obtained through single and multi-objective searches. Experimental results using a well-known testbed are provided to illustrate the performance of the operators and hence the effectiveness of the proposal.

  1. Gustavo Olague and Leonardo Trujillo. Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming. Image Vision Comput. 29(7):484-498, 2011. BibTeX

    @article{DBLP:journals/ivc/OlagueT11,
    	author = "Gustavo Olague and Leonardo Trujillo",
    	title = "Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming",
    	journal = "Image Vision Comput.",
    	volume = 29,
    	number = 7,
    	year = 2011,
    	pages = "484-498",
    	ee = "http://dx.doi.org/10.1016/j.imavis.2011.03.004",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    

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Genetic programming with one-point crossover and subtree mutation for effective problem solving and bloat control

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Abstract

Genetic programming (GP) is one of the most widely used paradigms of evolutionary computation due to its ability to automatically synthesize computer programs and mathematical expressions. However, because GP uses a variable length representation, the individuals within the evolving population tend to grow rapidly without a corresponding return in fitness improvement, a phenomenon known as bloat. In this paper, we present a simple bloat control strategy for standard tree-based GP that achieves a one order of magnitude reduction in bloat when compared with standard GP on benchmark tests, and practically eliminates bloat on two real-world problems. Our proposal is to substitute standard subtree crossover with the one-point crossover (OPX) developed by Poli and Langdon (Second online world conference on soft computing in engineering design and manufacturing, Springer, Berlin (1997)), while maintaining all other GP aspects standard, particularly subtree mutation. OPX was proposed for theoretical purposes related to GP schema theorems, however since it curtails exploration during the search it has never achieved widespread use. In our results, on the other hand, we are able to show that OPX can indeed perform an effective search if it is coupled with subtree mutation, thus combining the bloat control capabilities of OPX with the exploration provided by standard mutation.

  1. Leonardo Trujillo. Genetic programming with one-point crossover and subtree mutation for effective problem solving and bloat control. Soft Comput. 15(8):1551-1567, 2011. BibTeX

    @article{DBLP:journals/soco/Trujillo11,
    	author = "Leonardo Trujillo",
    	title = "Genetic programming with one-point crossover and subtree mutation for effective problem solving and bloat control",
    	journal = "Soft Comput.",
    	volume = 15,
    	number = 8,
    	year = 2011,
    	pages = "1551-1567",
    	ee = "http://dx.doi.org/10.1007/s00500-010-0687-7",
    	bibsource = "DBLP, http://dblp.uni-trier.de"
    }
    

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Automated design of image operators that detect interest points

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Abstract

This work describes how evolutionary computation can be used to synthesize low-level image operators that detect interesting points on digital images. Interest point detection is an essential part of many modern computer vision systems that solve tasks such as object recognition, stereo correspondence, and image indexing, to name but a few. The design of the specialized operators is posed as an optimization/search problem that is solved with genetic programming (GP), a strategy still mostly unexplored by the computer vision community. The proposed approach automatically synthesizes operators that are competitive with state-of-the-art designs, taking into account an operator's geometric stability and the global separability of detected points during fitness evaluation. The GP search space is defined using simple primitive operations that are commonly found in point detectors proposed by the vision community. The experiments described in this paper extend previous results (Trujillo and Olague, 2006a,b) by presenting 15 new operators that were synthesized through the GP-based search. Some of the synthesized operators can be regarded as improved manmade designs because they employ well-known image processing techniques and achieve highly competitive performance. On the other hand, since the GP search also generates what can be considered as unconventional operators for point detection, these results provide a new perspective to feature extraction research.

  1. Leonardo Trujillo and Gustavo Olague. Automated design of image operators that detect interest points. Evol. Comput. 16(4):483–507, 2008. URL, DOI BibTeX

    @article{Trujillo:2008:ADI:1479056.1479060,
    	author = "Trujillo, Leonardo and Olague, Gustavo",
    	title = "Automated design of image operators that detect interest points",
    	journal = "Evol. Comput.",
    	issue_date = "Winter 2008",
    	volume = 16,
    	number = 4,
    	month = "",
    	year = 2008,
    	issn = "1063-6560",
    	pages = "483--507",
    	numpages = 25,
    	url = "http://dx.doi.org/10.1162/evco.2008.16.4.483",
    	doi = "10.1162/evco.2008.16.4.483",
    	acmid = 1479060,
    	publisher = "MIT Press",
    	address = "Cambridge, MA, USA",
    	keywords = "Feature detection, computer vision, genetic programming, interest points"
    }
    

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