In this paper, two algorithms have been developed for allocation and size determination of Active Power Filters (APF) in power systems. In the first algorithm, the objective is to minimize harmonic voltage distortion. The objective in the second algorithm is to minimize the new APF injection currents while satisfying harmonic standards. Genetic algorithm is proposed for these two optimization p...

This paper presents new encoding methods for the binary genetic algorithm (BGA) and new converting methods for the real-coded genetic algorithm (RCGA). These methods are developed for the specific case in which some parameters have to be searched in wide ranges since their actual values are not known. The oversampling effect which occurs at large values in the wide range search are reduced by a...

This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized ver...

resource limitation in zero time may cause to some profitable projects not to be selected in project selection problem, thus simultaneous project portfolio selection and scheduling problem has received significant attention. in this study, budget, investment costs and earnings are considered to be stochastic. the objectives are maximizing net present values of selected projects and minimizing v...

wireless sensor networks (wsns) consist of a large number of sensor nodes which are capable of sensing different environmental phenomena and sending the collected data to the base station or sink. since sensor nodes are made of cheap components and are deployed in remote and uncontrolled environments, they are prone to failure; thus, maintaining a network with its proper functions even when und...

This paper describes the implementation details and compares two methods for optimisation of multi-dimensional cost functions. The implemented genetic algorithm uses two chromosome representations: binary and floating point. In both representations the algorithm is based on steady-state reproduction, roulette-wheel bad individuals selection and has the same parameters.

This paper addresses a novel microwave tomography method for the reconstruction of the dielectric properties of breast tissue using the percentage of the tissue water content. The reconstruction algorithm consists of a combination of a binary and a real Genetic Algorithm (GA).

in this study, a hybrid algorithm is presented to tackle multi-variables robust design problem. the proposed algorithm comprises neural networks (nns) and co-evolution genetic algorithm (cga) in which neural networks are as a function approximation tool used to estimate a map between process variables. furthermore, in order to make a robust optimization of response variables, co-evolution algor...

In this work we present an algorithm to the unrestricted binary quadratic program. This approach combines genetic operators with greedy and heuristic procedures. We started from a genetic based algorithm and replaced the random mutation by a greedy procedure based on each variable contribution to the objective function. We also introduced in the genetic population an individual obtained by a he...

This paper explores the possibility of using genetic algorithms to invert seismic refraction travel time data for a two-dimensional velocity structure. The problem is usually high-dimensional, non-linear and multi-modal. Our chromosome is encoded as a two-dimensional real number array. Binary tournament selection, two-point crossover and block mutation directed by prior geological knowledge are...