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Electronic commerce (e-commerce) has grown rapidly over the past years. Products, services and information of different types are offered daily for many Internet users. Finding out an appropriate strategy to offer a product to each customer in a personalized fashion is the goal of a recommender system. This association between items is a task that falls under the umbrella of data mining, more specifically...
Finding Nash equilibria has been one of the early objectives of research in game theory, and still represents a challenge to this day. We introduce a multiobjective formulation for computing Pareto-optimal sets of mixed Nash equilibria in normal form games. Computing these sets can be notably useful in decision making, because it focuses the analysis on solutions with greater outcome and hence more...
Unsupervised models can provide supplementary soft constraints to help classify new data since similar instances are more likely to share the same class label. In this context, we investigate how to make an existing algorithm, named C3E (from Combining Classifier and Cluster Ensembles), more user-friendly by automatically tunning its main parameters with the use of metaheuristics. In particular, the...
Estimation of Distribution Algorithms (EDA) are stochastic population based search algorithms that use a distribution model of the population to create new candidate solutions. One problem that directly affects the EDAs' ability to find the best solutions is the premature convergence to some local optimum due to diversity loss. Inspired by the Random Immigrants technique, this paper presents the Bayesian...
This paper explores, with numerical case studies, the performance of an optimization algorithm that is a variant of EPSO, the Evolutionary Particle Swarm Optimization method. EPSO is already a hybrid approach that may be seen as a PSO with self-adaptive weights or an Evolutionary Programming approach with a self-adaptive recombination operator. The new hybrid DEEPSO retains the self-adaptive properties...
This paper provides a brief description on how continuous algorithms can be applied to binary problems. Differential Evolution is the continuous algorithm studied and two versions of this algorithm are presented: the Binary Differential Evolution with a binary encoding and the Discretized Differential Evolution with a continuous encoding. Several discretization methods are presented and the most used...
This paper presents a Copula-based Estimation of Distribution Algorithm with Parameter Updating for numeric optimization problems. This model implements an estimation of distribution algorithm using a multivariate extension of the Archimedean copula (MEC-EDA) to estimate the conditional probability for generating a population of individuals. Moreover, the model uses traditional crossover and elitism...
This paper utilises Evolved Linker Gene Expression Programming (EL-GEP), a new variant of Gene Expression Programming (GEP), to solve symbolic regression and sequence induction problems. The new technique was first proposed in [6] to evolve modularity in robotic behaviours. The technique extends the GEP algorithm by incorporating a new alphabetic set (linking set) from which genome linking functions...
Differential evolution (DE) was originally designed to solve continuous optimization problems, but recent works have been investigating this algorithm for tackling combinatorial optimization (CO), particularly in permutation-based combinatorial problems. However, most DE approaches for combinatorial optimization are not general approaches to CO, being exclusive for per mutational problems and often...
This paper investigates various strategies for the management of solution space diversity within the context of a meta-hyper heuristic algorithm. The adaptive local search meta-hyper heuristic (ALSHH), which adaptively applies a local search algorithm when the population diversity strays outside a predetermined solution space diversity profile, is proposed. ALSHH was shown to compare favourably with...
In this paper, we propose a hybrid Multiobjective Estimation of Distribution Algorithm based on Artificial Bee Colonies and Clusters (MOEDABC) to solve multiobjective optimization problems with continuous variables. This algorithm is inspired in the organization and division of work in a bee colony and employs techniques from estimation of distribution algorithms. To improve some estimations we also...
Analog integrated circuits design is a complex task due to the large number of input variables that must be determined in order to achieve different design goals such as voltage gain, unit voltage gain frequency, phase margin and dissipated power. This paper describes and implements an evolutionary optimization solution based on genetic algorithms and the well-known SPICE simulator, named "AGSPICE/FEI",...
Periodic routines have been traditionally identified in Social Sciences as the essential component of social organizations that are persistent in time, with the temporal continuity of such routines constituting the foundation of the preservation of the social systems both between successive generations and between extant and immigrant populations. Open multiagent systems (MAS) with persistent social...
Finding a good clustering solution for an unknown problem is a challenging task. Evolutionary algorithms have proved to be reliable methods to search for high quality solutions to complex problems. The present paper proposes a new set of genetic operators for the Fast Evolutionary Algorithm for Clustering (Fast-EAC) to improve the solution quality and computational efficiency. The new algorithm, called...
This paper applies a heterogeneous parallel ecology-inspired algorithm (pECO) to solve a complex problem from bioinformatics. The ecological-inspired algorithm represents a new perspective to develop cooperative evolutionary algorithms. Different algorithms are applied to compose the computational ecosystem in a heterogeneous model. The aim is to search low energy conformations for the Protein Structure...
This paper introduces a new approach to building sparse least square support vector machines (LSSVM) based on genetic algorithms (GAs) for classification tasks. LSSVM classifiers are an alternative to SVM ones due to the training process of LSSVM classifiers only requires to solve a linear equation system instead of a quadratic programming optimization problem. However, the lost of sparseness in the...
The complex regimes of operation situated between ordered and chaotic behavior are hypothesized to give rise to computational capabilities. Lacking an universal blueprint for the emergence of complexity, a costly search is typically used to find the configurations of distributed artificial systems that can facilitate global computation. In this paper, we address the tedious task of searching for complex...
This work delves into variations of FSS that uses local information (i.e. fish weights) for splitting the school and presents comparative analyses of the new method, tried here in three ways. Hence, this is an attempt to create a more economical alternative for the best performing multimodal version of the algorithm FSS, the dFSS. The work capitalizes on some modifications in the Collective Instinctive...
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