Biological-based genetic algorithms for optimized disaster response resource allocation

An effective disaster response requires rapid coordination of existing resources, which can be considered a resource optimization problem. Genetic algorithms (GAs) have been proven effective for solving optimization problems in various fields. However, GAs essentially use generation succession to search for optimal solutions. Therefore, their use of reproduction, crossover, and mutation operations may exclude optimal chromosomes during generation succession and prevent full use of previous search experience. Meanwhile, premature convergence caused by inadequate diversity of chromosome populations limits the search to a local optimum. Genetic algorithms also incur high computational costs. The biological-based GAs (BGAs) proposed in this study address these problems by including mechanisms for elite reserve areas, nonlinear fitness value conversion, and migration. This study performed experimental simulations to compare BGAs with immune algorithms (IAs) and GAs in terms of effectiveness for allocating disaster refuge site staff and for planning relief supply distribution. The simulation results show that, compared to other methods, BGAs can compute optimal solutions faster. Therefore, they provide a more useful reference when performing the decision-making needed to solve disaster response resource optimization problems.     

Development of a real-time non-intrusive appliance load monitoring system: An application level model

The research “Behavior Change and Energy Use” (US Department of Energy and Climate Change, 2011) [1] shows that with better information in the monthly electricity bill, the Energy Performance Certificate (EPC) can encourage people to reduce their energy usage. That is why smart meters - the emerging technology to help people to know their monthly energy consumption, are gradually replacing mechanical power meters. In this paper, we investigate a special energy monitoring process named Non-Intrusive Appliance Load Monitoring (NIALM), which is potentially the best method to give consumers pertinent information with respect to power consumption. However, real-time feedback feature in a low cost NIALM system is still a big challenge in such technology because of the complication in NIALM’s algorithms. System on Chip (SoC) technology can solve this challenge. Besides including high-speed interconnection and multi-processors, integrating Field-Programmable Gate Array (FPGA) into SoCs may be the most important evolution, which provides developers a powerful tool to develop a low cost but high performance system. Therefore, in this paper we proposed a development of a real-time NIALM system based on the SoC with FPGA acceleration.     

A tribe competition-based genetic algorithm for feature selection in pattern classification

Feature selection has always been a critical step in pattern recognition, in which evolutionary algorithms, such as the genetic algorithm (GA), are most commonly used. However, the individual encoding scheme used in various GAs would either pose a bias on the solution or require a pre-specified number of features, and hence may lead to less accurate results. In this paper, a tribe competition-based genetic algorithm (TCbGA) is proposed for feature selection in pattern classification. The population of individuals is divided into multiple tribes, and the initialization and evolutionary operations are modified to ensure that the number of selected features in each tribe follows a Gaussian distribution. Thus each tribe focuses on exploring a specific part of the solution space. Meanwhile, tribe competition is introduced to the evolution process, which allows the winning tribes, which produce better individuals, to enlarge their sizes, i.e. having more individuals to search their parts of the solution space. This algorithm, therefore, avoids the bias on solutions and requirement of a pre-specified number of features. We have evaluated our algorithm against several state-of-the-art feature selection approaches on 20 benchmark datasets. Our results suggest that the proposed TCbGA algorithm can identify the optimal feature subset more effectively and produce more accurate pattern classification.     

Analysis of human chromosome classification using centromere position

Automated homologous classification of human chromosomes from the metaphase spread of a cell is an essential task to identify various genetic disorders. Karyotyping is the process of visualizing and categorizing the chromosomes within a cell. It is very important to have a good feature extraction scheme for proper chromosome classification. Chromosome classification is performed by identifying the morphological features which mainly include the centromere location. The parameter is essential for classification which helps in determining a karyogarm. In this paper, a comparative analysis of two algorithms for identification of centromere position is done. The two approaches make use of the Medial Axis Transformation and Projection Vector. The analysis helps us to identify the algorithm that performs better in the identification of centromere position. The projection vector approach performs well in obtaining the centromere location in the chromosome with an efficiency of 90%.     

Neuro-evolutionary approach applied for optimizing the PEMFC performance

A multi-objective optimization strategy, based on stacked neural network–genetic algorithm (SNN–GA) hybrid approach, was applied to study the C/PBI content on a high temperature PEMFC performance. The operating conditions of PEMFC were correlated with power density and electrochemical active surface area for electrodes. The structure of the stack was determined in an optimal form related to the contribution of individual neural networks, after applying an interpolation based procedure. Multi-objective optimization using SNN as model and GA as solving procedure provides optimal working conditions which lead to a high PEMFC performance. Simulation results were in agreement with experimental data, both for model validation and system optimization (the C/PBI content in the range of 17–21%).     

Selecting Features that Drive Internationalization of Spanish Firms

Abstract

Internationalization is nowadays common for many large enterprises and as a result Foreign Direct Investment (FDI) flows are increasing. Bilateral psychic distance stimuli between home and host countries and vicarious experience are analyzed, together with other firm- and country-level FDI determinants, by means of a feature selection model to gain deep knowledge about the internationalization strategy of Spanish large firms. Wrapper feature selection based on a genetic algorithm is applied to identify the most important features leading to internationalization decision. Additionally, obtained results are compared to those obtained by logistic regressions (Standard, Rare Event, and Conditional).     

Backtracking search optimization heuristics for nonlinear Hammerstein controlled auto regressive auto regressive systems

In this work, novel application of evolutionary computational heuristics is presented for parameter identification problem of nonlinear Hammerstein controlled auto regressive auto regressive (NHCARAR) systems through global search competency of backtracking search algorithm (BSA), differential evolution (DE) and genetic algorithms (GAs). The mean squared error metric is used for the fitness function of NHCARAR system based on difference between actual and approximated design variables. Optimization of the cost function is conducted with BSA for NHCARAR model by varying degrees of freedom and noise variances. To verify and validate the worth of the presented scheme, comparative studies are carried out with its counterparts DE and GAs through statistical observations by means of weight deviation factor, root of mean squared error, and Thiel’s inequality coefficient as well as complexity measures.     

A multi-item transportation problem with fuzzy tolerance

This paper presents the recently introduced modified subgradient method for optimization and its effectiveness in a fuzzy transportation model. Here a multi-item balanced transportation problem (MIBTP) is formulated where unit transportation costs are imprecise. Also available spaces and budgets at destinations are limited but imprecise. The objective is to find a shipment schedule for the items that minimizes the total cost subjected to imprecise warehouse and budget constraints at destinations. The proposed model is reduced to a multi-objective optimization problem using tolerances, then to a crisp single-objective one using fuzzy non-linear programming (FNLP) technique and Zimmermann's method. The above fuzzy MIBTP is also reduced to another form of deterministic one using modified sub-gradient method (MSM). These two crisp optimization problems are solved by Genetic Algorithm (GA). As an extension, fuzzy multi-item balanced solid transportation problems (STPs) with and without restrictions on some routes and items are formulated and reduced to deterministic ones following FNLP and Zimmermann's methods. These models are also solved by GA. Models are illustrated numerically, optimum results of fuzzy MIBTP from two deductions are compared. Results are also presented for different GA parameters.     

Special Genetic Identification Algorithm with smoothing in the frequency domain

Due to the increase in speed and lightweight construction, modern robots vibrate significantly during motion. Thus, accurate mechanical modeling and detailed controller behavior is essential for accurate path planning and control design of robots. For the suppression of undesired vibrations detailed models are used to develop robust controllers. Least square identification methods require deep insight in the analytical equations and thus are not very suitable for identification of different highly nonlinear robot models. Recently, we presented our genetic parameter identification in Brussels, Ludwig and Gerstmayr (2011). It minimizes the error of measured and simulated quantities. Highly efficient models in the multibody system tool HOTINT lead to short computational times for various simulations with different parameters. The simulation models can easily be assembled by engineers without a detailed knowledge of the underlying multibody system. As drawback of genetic optimization, many sub-minima were detected. Many simulations were required for the determination of the global minimum. Our current approach was to extend our previous algorithm. Measured and simulated quantities are transformed into the frequency domain. In contrast to previous work, Ludwig and Gerstmayr (2013), amplitude spectra of measured and simulated quantities are smoothed prior to the L2-norm computation. The presented method is tested using small scale test problems as well as real robots. Smoothing in the frequency domain leads to a smaller number of simulations needed for obtaining higher accuracy. It turns out that the presented algorithm is more accurate and precise than a standard algorithm and reduces the computational cost.     

Implementation of neuro-fuzzy system with modified high performance genetic algorithm on embedded systems

In this paper implementation of ANFIS on embedded systems based on single-core and multi-core ARM processors is presented. A novel evolutionary optimization tool named, modified high performance genetic algorithm (mHPGA) with bacterial conjugation operator is applied to ANFIS as a training method. Fixed point and floating point number representations are applied and compared. Moreover new mutation algorithm has been proposed for fixed point numbers. The proposed method is designed to sweep numbers space to search possible solutions in large state space. Concurrency nature of mHPGA benefits implementation of multi threading feature on ARM cortex-A53 with four cores.