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Support vector machine (SVM) is a powerful tool for classification and regression problems, however, its time and space complexities make it unsuitable for large datasets. In this paper, we present GeneticSVM, an evolutionary computing based distributed approach to find optimal solution of quadratic programming (QP) for kernel support vector machine. In Ge-neticSVM, novel encoding method and crossover...
This paper presents a novel supervised clustering technique including different clustering algorithms which cooperate together to span the decision space in a supervised manner. It uses a variety of clustering methods for an efficient partitioning. An evolutionary algorithm is used to tune the key parameters of the cooperative scheme which minimizes an error-based objective function on the training...
In this paper, it is experimentally verified that TDGA (Thermo Dynamical Genetic Algorithm) is effective in solving a function optimization problem using Genetic Algorithms, because of its sustainability of population diversity and efficiency of searching for solutions. We experimentally and quantitatively verify the hypothesis that we can improve the ratio of searching for the optimum solution and...
We construct a method of time warping in quasiperiodic time series analysis using genetic algorithm in order to extract the instantaneous phase difference between a template signal and a testing signal. Contrast to previous studies, which involves correlation estimations to determine the shape similarity of two signals taken from the quasiperiodic time series, time warping perform the comparison of...
The rearrangement of genomes is an important tool for studying the evolution of genomes and specifically for the construction of phylogenies. A translocation splits and combines the strings of genes of a pair of chromosomes inside a genome and is considered a suitable operation for rearrangement of genomes with multiple chromosomes. The translocation distance between two genomes is the minimum number...
This work presents different opposite learning strategies for Ant Knapsack, an ant based algorithm for the Multidimensional Knapsack Problem. We propose to include a previous opposite learning phase to Ant Knapsack, for discarding regions of the search space. This opposite knowledge is then used by Ant Knapsack for solving the original problem. The objective is to improve the search process of Ant...
Web Service Composition (WSC) is a prominent way of actualizing service-oriented architecture by integrating network-accessible Web services into a new invokable application. Evolutionary computation techniques have provided rewarding approaches in automatic Web service composition over the last decade. However, the studies on considering both functionality and non-functionality (i.e. Quality-of-Service,...
In Job Shop Scheduling (JSS) problems, there are usually many conflicting objectives to consider, such as the makespan, mean flowtime, maximal tardiness, number of tardy jobs, etc. Most studies considered these objectives separately or aggregated them into a single objective (fitness function) and treat the problem as a single-objective optimization. Very few studies attempted to solve the multi-objective...
In this paper, a new moving block sequence (MBS) representation for resource-constrained project scheduling problems (RCPSPs) is proposed, which is different from the classical activity list that has been widely used for RCPSPs. An activity in a project of RCPSPs has fixed duration and resource demands, thus, it can be modeled as a rectangle block whose height represents the resource demands and width...
Resource constrained project scheduling problem (RCPSP) is a well known problem in the area of discrete optimization. It involves scheduling a given set of activities such that they are completed within minimum possible time, while satisfying a given set of precedence and resource constraints. RCPSP has a wide applicability in a number of industries, such as engineering, management, software, etc...
The classification performance of a weighted voting ensemble of classifiers largely depends on the proper weight chosen for each base classifier's vote. In this paper, we propose the use of Differential Evolution algorithm for adjustment of voting-weights of base classifiers used in a heterogeneous ensemble of classifiers (HEoC). We used the average Matthews Correlation Coefficient (MCC), calculated...
Load Patterns (LPs) clustering has a broad range of applications, such as tariff formulation, power system planning, load forecasting, and so on. In this paper, we develop a multi-objective version of Differential Evolution (DE) using a Pareto Tournament (PT) selection to solve the LP clustering problem. Our automatic DE LP clustering (ADE-LPC) algorithm provides an entire Pareto front, and by incorporating...
Performing data mining tasks on raw time series is inefficient as these data are high-dimensional by nature. Instead, time series are first pre-processed using several techniques before the different data mining tasks can be performed. In general, there are two main approaches to pre-process time series. The first is what we call landmark methods. These methods are based on finding characteristic...
Recommender systems are methods built to actively suggest personalized items to users based on their explicit declared preferences (ratings of movies in Netflix), or implicitly observed actions (purchase history). Although a great number of recommendation methods have been previously proposed in the literature, in many problems these methods present a high degree of disagreement in their recommendations...
Document clustering is useful for many research areas such as Text Mining and Information Retrieval. Therefore, it is desirable to be able to cluster documents accurately. The clustering quality depends not only on the clustering algorithm used but also on the way text is represented in the algorithm. Text is typically represented using the All-Words Vector Space Model in text mining applications...
Entity matching is to map the records to the corresponding entity. It is a well known problem studied by many researchers over the last few years. In bibliographic database, the data evolve over time. For example, the email id of an author in DBLP and ArnetMiner which are two bibliographic databases changes with time. Authors also keep on changing their affiliations. The set of authors with whom they...
Nowadays an effective Energy Storage System (ESS) is a fundamental requirement for any effective innovation in the fields of energetic and transportation sustainability. One of the most important device for obtaining efficient ESSs is the Battery Management System (BMS). It includes all the electronic components and algorithms for the monitoring and management of the ESS status. The key task of the...
This paper proposes a hybrid approach to optimal day-ahead pricing for demand response management. At the customer-side, a comprehensive energy management system, which includes most commonly used appliances and an effective waiting time cost model is proposed to manage the energy usages in households (lower level problem). At the retailer-side, the best retail prices are determined to maximize the...
In this paper, the unsupervised approach recently proposed by the authors for automatic leakage detection in smart water grids is extended. First of all, the EPANET tool is adopted in order to simulate more realistic leakages. Also, with respect to the original work, an additional time resolution, of 30 minutes, is included, based on the water dataset of the Almanac of Minutely Power Dataset (AMPds)...
Among the many electrical load disaggregation methods, often referred to as Non-Intrusive Load Monitoring techniques, the Additive Factorial Approximate MAP (AFAMAP) algorithm has shown outstanding capabilities and, therefore, it is nowadays regarded as a reference model. In order to achieve more accurate disaggregation results, and to satisfy real life environment requirements, further improvements...
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