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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 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...
The shortest common superstring problem has important applications in computational biology (e.g. genome assembly) and data compression. This problem is NP-hard, but several heuristic algorithms proved to be efficient for this problem. For example, for the algorithm known as GREEDY it was shown that, if the optimal superstring has the length of N, it produces an answer with length not exceeding 3...
Within functional verification of digital systems there are dynamic methods based on Device Under Verification simulation. We focus on this type of method using functional coverage points. Nowadays, the main problem consists in obtaining high values to exercise all functional coverage points in the device. In this paper we propose a heuristic dynamic verification method based on a Binary Differential...
The employment of genetic algorithms in parameters optimization of direct-detection optical orthogonal frequency division multiplexing (DDO-OFDM) systems in short-range links is reported. Experimental transmission of a 3.56 Gb/s (4-QAM subcarrier mapping) optimized DDO-OFDM system in optical back-to-back (B2B) configuration and through 20 and 40 km of uncompensated standard single-mode fiber (SSMF)...
A recent nature inspired optimization algorithm, Fish School Search (FSS) is applied to the finite element model (FEM) updating problem. This method is tested on a GARTEUR SM-AG19 aeroplane structure. The results of this algorithm are compared with two other metaheuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It is observed that on average, the FSS and PSO algorithms...
This work deals with satellite sun sensor placement using genetic algorithms. For a simplified but realistic problem scenario, this problem is solved and many simulation results are shown. The proposed methodology, which relies on numerical shadow analysis and multi-objective optimization, is discussed and a typical satellite design problem, that is frequently solved by a try-and-error approach, is...
This work presents the implementation of an agent based model concept to simulate a sample of the German society under a governmental social transfer system. Subsequently the behavior of the model is analyzed under changing conditions in order to proof that it can be used for the simulation of real societies under similar conditions. An important objective is to give evidence on economic interdependencies...
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",...
The purpose of this work is to apply a hybrid algorithm based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) for solving the problem of Economic Dispatch, which is based on supplying an energy demand, subjected to some restriction and reach out the best possible cost. Basically, we use the mutation operator from GAs aiming to explore regions in the search space that cannot be reached...
Power Distribution Network Reconfiguration demands the change of current state of the network in order to reach optimal operation according to some previouly defined figures of merit. This paper presents a new methodology based on Multi-Agent Systems for power distribution network reconfiguration aiming at minimizing power losses based on game theory. The principal characteristic of the game is the...
Artificial Neural Networks (ANN) have been widely used in time series forecasting problem. However, a more promising approach is the combination of ANN with other intelligent techniques, such as genetic algorithms, evolutionary strategies, etc, where these evolutionary algorithms have the objective of train and adjust all parameter of the ANN. In the evolutionary process is necessary define a fitness...
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...
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