@article {8049471,
	title = {Mining Context-Aware Association Rules Using Grammar-Based Genetic Programming},
	journal = {IEEE Transactions on Cybernetics},
	volume = {48},
	number = {11},
	year = {2018},
	month = {Nov},
	pages = {3030-3044},
	abstract = {Real-world data usually comprise features whose interpretation depends on some contextual information. Such contextual-sensitive features and patterns are of high interest to be discovered and analyzed in order to obtain the right meaning. This paper formulates the problem of mining context-aware association rules, which refers to the search for associations between itemsets such that the strength of their implication depends on a contextual feature. For the discovery of this type of associations, a model that restricts the search space and includes syntax constraints by means of a grammar-based genetic programming methodology is proposed. Grammars can be considered as a useful way of introducing subjective knowledge to the pattern mining process as they are highly related to the background knowledge of the user. The performance and usefulness of the proposed approach is examined by considering synthetically generated datasets. A posteriori analysis on different domains is also carried out to demonstrate the utility of this kind of associations. For example, in educational domains, it is essential to identify and understand contextual and context-sensitive factors that affect overall and individual student behavior and performance. The results of the experiments suggest that the approach is feasible and it automatically identifies interesting context-aware associations from real-world datasets.},
	keywords = {Association rules, Computer science, context awareness, context-aware association rules mining, context-sensitive factors, contextual features, contextual information, contextual-sensitive features, data mining, Feature extraction, genetic algorithms, Genetic programming, Grammar, grammar-based genetic programming methodology, grammars, pattern mining process, Proposals, ubiquitous computing},
	issn = {2168-2267},
	doi = {10.1109/TCYB.2017.2750919},
	author = {J. M. Luna and M. Pechenizkiy and M. J. del Jesus and S. Ventura}
}
@article {BERLANGA20101183,
	title = {GP-COACH: Genetic Programming-based learning of Compact and ACcurate fuzzy rule-based classification systems for High-dimensional problems},
	journal = {Information Sciences},
	volume = {180},
	number = {8},
	year = {2010},
	pages = {1183 - 1200},
	abstract = {In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.},
	keywords = {classification, Fuzzy rule-based systems, Genetic Fuzzy Systems, Genetic programming, High-dimensional problems, Interpretability-accuracy trade-off},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2009.12.020},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025509005635},
	author = {F.J. Berlanga and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera}
}
@article {SANCHEZ2008952,
	title = {Obtaining transparent models of chaotic systems with multi-objective simulated annealing algorithms},
	journal = {Information Sciences},
	volume = {178},
	number = {4},
	year = {2008},
	pages = {952 - 970},
	abstract = {Transparent models search for a balance between interpretability and accuracy. This paper is about the estimation of transparent models of chaotic systems from data, which are accurate and simple enough for their expression to be understandable by a human expert. The models we propose are discrete, built upon common blocks in control engineering (gain, delay, sum, etc.) and optimized both in their complexity and accuracy. The accuracy of a discrete model can be measured by means of the average error between its prediction for the next sampling period and the true output at that time, or {\textquoteleft}one-step error{\textquoteright}. A perfect model has zero one-step error, but a small error is not always associated with an approximate model, especially in chaotic systems. In chaos, an arbitrarily low difference between two initial states will produce uncorrelated trajectories, thus a model with a low one-step error may be very different from the desired one. Even though a recursive evaluation (multi-step prediction) improves the fitting, in this work we will show that a learning algorithm may not converge to an appropriate model, unless we include some terms that depend on estimates of certain properties of the model (so called {\textquoteleft}invariants{\textquoteright} of the chaotic series). We will show this graphically, by means of the reconstructed attractors of the original system and the model. Therefore, we also propose to follow a multi-objective approach to model chaotic processes and to apply a simulated annealing-based optimization to obtain transparent models.},
	keywords = {Chaotic systems, Genetic programming, Multi-objective simulated annealing, Transparent models},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2007.09.029},
	url = {http://www.sciencedirect.com/science/article/pii/S002002550700463X},
	author = {Luciano S{\'a}nchez and Jos{\'e} R. Villar}
}
@article {SANCHEZ2001175,
	title = {Combining GP operators with SA search to evolve fuzzy rule based classifiers},
	journal = {Information Sciences},
	volume = {136},
	number = {1},
	year = {2001},
	note = {Recent Advances in Genetic Fuzzy Systems},
	pages = {175 - 191},
	abstract = {The genotype{\textendash}phenotype encoding of fuzzy rule bases in GA, along with their corresponding crossover and mutation operators, can be used by other search schemes, improving the behavior of these last ones. As a practical consequence of this, a simulated annealing-based method for inducting both parameters and structure of a fuzzy classifier has been developed. The adjacency operator in SA has been replaced with a macromutation taken from tree-shaped genotype GAs. We will show that results of SA search are similar to those of GP in both the efficiency of the learned classifiers and in its linguistic interpretability, while the memory consumption of the learning process is lower.},
	keywords = {Fuzzy classification, genetic algorithms, Genetic programming, Simulated annealing},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/S0020-0255(01)00146-3},
	url = {http://www.sciencedirect.com/science/article/pii/S0020025501001463},
	author = {Luciano S{\'a}nchez and In{\'e}s Couso and J.A. Corrales}
}
@article {843495,
	title = {Interval-valued GA-P algorithms},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {4},
	number = {1},
	year = {2000},
	month = {April},
	pages = {64-72},
	abstract = {When genetic programming (GP) methods are applied to solve symbolic regression problems, we obtain a point estimate of a variable, but it is not easy to calculate an associated confidence interval. We designed an interval arithmetic-based model that solves this problem. Our model extends a hybrid technique, the GA-P method, that combines genetic algorithms and genetic programming. Models based on interval GA-P can devise an interval model from examples and provide the algebraic expression that best approximates the data. The method is useful for generating a confidence interval for the output of a model, and also for obtaining a robust point estimate from data which we know to contain outliers. The algorithm was applied to a real problem related to electrical energy distribution. Classical methods were applied first, and then the interval GA-P. The results of both studies are used to compare interval GA-P with GP, GA-P, classical regression methods, neural networks, and fuzzy models.},
	keywords = {Arithmetic, Computer science, confidence interval, electrical energy distribution, Fuzzy neural networks, Fuzzy systems, genetic algorithms, Genetic programming, Neural networks, point estimate, Robustness, statistical analysis, symbol manipulation, symbolic regression},
	issn = {1089-778X},
	doi = {10.1109/4235.843495},
	author = {L. S{\'a}nchez}
}
