Browsing by Keyword "Harmony Search"
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Item A Coral Reefs Optimization algorithm with Harmony Search operators for accurate wind speed prediction(2015-03-01) Salcedo-Sanz, Sancho; Pastor-Sanchez, Alvaro; Del Ser, Javier; Prieto, Luis; Geem, Zong-Woo; IAThis paper introduces a new hybrid bio-inspired solver which combines elements from the recently proposed Coral Reefs Optimization (CRO) algorithm with operators from the Harmony Search (HS) approach, which gives rise to the coined CRO-HS optimization technique. Specifically, this novel bio-inspired optimizer is utilized in the context of short-term wind speed prediction as a means to obtain the best set of meteorological variables to be input to a neural Extreme Learning Machine (ELM) network. The paper elaborates on the main characteristics of the proposed scheme and discusses its performance when predicting the wind speed based on the measures of two meteorological towers located in USA and Spain. The good results obtained in these experiments when compared to naïve versions of the CRO and HS algorithms are promising and pave the way towards the utilization of the derived hybrid solver in other optimization problems arising from diverse disciplines.Item Hybridizing Cartesian Genetic Programming and Harmony Search for adaptive feature construction in supervised learning problems(2016) Elola, Andoni; Del Ser, Javier; Bilbao, Miren Nekane; Perfecto, Cristina; Alexandre, Enrique; Salcedo-Sanz, Sancho; IAThe advent of the so-called Big Data paradigm has motivated a flurry of research aimed at enhancing machine learning models by following very diverse approaches. In this context this work focuses on the automatic construction of features in supervised learning problems, which differs from the conventional selection of features in that new characteristics with enhanced predictive power are inferred from the original dataset. In particular this manuscript proposes a new iterative feature construction approach based on a self-learning meta-heuristic algorithm (Harmony Search) and a solution encoding strategy (correspondingly, Cartesian Genetic Programming) suited to represent combinations of features by means of constant-length solution vectors. The proposed feature construction algorithm, coined as Adaptive Cartesian Harmony Search (ACHS), incorporates modifications that allow exploiting the estimated predictive importance of intermediate solutions and, ultimately, attaining better convergence rate in its iterative learning procedure. The performance of the proposed ACHS scheme is assessed and compared to that rendered by the state of the art in a toy example and three practical use cases from the literature. The excellent performance figures obtained in these problems shed light on the widespread applicability of the proposed scheme to supervised learning with legacy datasets composed by already refined characteristics.Item On the heritability of dandelion-encoded harmony search heuristics for tree optimization problems(IEEE, 2015-09-24) Perfecto, Cristina; Bilbao, Miren Nekane; Del Ser, Javier; Ferro, Armando; IATree based optimization problems stand for those paradigms where solutions can be arranged within a tree-like graph whose nodes represent the optimization variables of the problem at hand and their interconnecting edges topological and/or hierarchical relationships between such variables. In this context, a research line of increasing interest during the last decade focuses on the derivation of intelligent solution encoding strategies capable of 1) capturing all topological constraints of this particular class of graphs; and 2) preserving their connectivity properties when they undergo combination/mutation operations within approximative evolutionary solvers. This manuscript takes a step over the state of the art by shedding light on the heri-tability properties of the Dandelion tree encoding approach under avant-garde stochastically-controlled evolutionary operators. In particular we elaborate on the topological heritability of the so-called Harmony Memory Considering Rate (HMCR) exploitative operator of the Harmony Search algorithm, a population-based meta-heuristic algorithm that has so far shown to outperform other evolutionary schemes in a wide range of optimization scenarios. Results from extensive Monte Carlo simulations are discussed in terms of the preserved structural properties of the newly produced solutions with respect to the initial Dandelion-encoded population.Item One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms(2015-07-15) Salcedo-Sanz, Sancho; Muñoz-Bulnes, J.; Portilla-Figueras, Jose Antonio; Del Ser, Javier; IAThis paper elaborates on a problem of one-year ahead estimation of energy demand based on macroeconomic variables. To this end, two different Computational Intelligence approaches are herein evaluated: (1) a modified Harmony Search (HS) optimization algorithm with an exponential prediction model and (2) an Extreme Learning Machine (ELM). In the case of the HS, a feature selection of the best set of features for the prediction is carried out jointly with the optimization of the model’s parameters. On the other hand, the ELM will be tested with and without the feature selection carried out by the HS approach. We describe several modifications on the proposed HS, which include a hybrid encoding with a binary part for the feature selection, and a real part to tune the parameters of the prediction model. Other adaptations focused on the HS operators are also introduced. The performance of both approaches has been assessed in a real application scenario, corresponding to the total energy demand estimation in Spain, in which we have 14 macroeconomic variables with history values for the last 30 years, including the recent crisis period starting in 2008. The performance of the proposed HS and ELM models incorporating feature selection is shown to provide an accurate one-year-ahead forecast at a higher prediction’s accuracy when compared to previous proposals in the literature. Specifically, the HS and ELM approaches are able to improve the results of a previous approach (based on a genetic algorithm), obtaining an improvement over 15% in this problem of energy demand estimation. As a final experimental evaluation of the proposed algorithm, a similar problem of one-year ahead CO 2 emissions estimation from macro-economic variables is also tackled, and also in this case the HS and ELM are able to obtain significant improvements over a previous approach based on evolutionary computation, over 10% of improvement in this problem.