Examining the Impact of Neutral Theory on Genetic Algorithm Population Evolution

2015 ◽  
Author(s):  
Seamus Hill ◽  
Colm O'Riordan
Keyword(s):  
Genetic Algorithm ◽  
Neutral Theory ◽  
Population Evolution ◽  
The Impact

scholarly journals An Improved Adaptive Genetic Algorithm for Two-Dimensional Rectangular Packing Problem

2021 ◽  
Vol 11 (1) ◽  
pp. 413
Author(s):  
Yi-Bo Li ◽  
Hong-Bao Sang ◽  
Xiang Xiong ◽  
Yu-Rou Li
Keyword(s):  
Genetic Algorithm ◽  
Packing Problem ◽  
Global Perspective ◽  
Adaptive Genetic Algorithm ◽  
Two Dimensional ◽  
Filling Rate ◽  
Zero Waste ◽  
Population Evolution ◽  
Rectangular Packing ◽  
The Impact

This paper proposes the hybrid adaptive genetic algorithm (HAGA) as an improved method for solving the NP-hard two-dimensional rectangular packing problem to maximize the filling rate of a rectangular sheet. The packing sequence and rotation state are encoded in a two-stage approach, and the initial population is constructed from random generation by a combination of sorting rules. After using the sort-based method as an improved selection operator for the hybrid adaptive genetic algorithm, the crossover probability and mutation probability are adjusted adaptively according to the joint action of individual fitness from the local perspective and the global perspective of population evolution. The approach not only can obtain differential performance for individuals but also deals with the impact of dynamic changes on population evolution to quickly find a further improved solution. The heuristic placement algorithm decodes the rectangular packing sequence and addresses the two-dimensional rectangular packing problem through continuous iterative optimization. The computational results of a wide range of benchmark instances from zero-waste to non-zero-waste problems show that the HAGA outperforms those of two adaptive genetic algorithms from the related literature. Compared with some recent algorithms, this algorithm, which can be increased by up to 1.6604% for the average filling rate, has great significance for improving the quality of work in fields such as packing and cutting.

scholarly journals Optimized Strategic Planning of Future Norwegian Low-Voltage Networks with a Genetic Algorithm Applying Empirical Electric Vehicle Charging Data

Electricity ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 91-109
Author(s):  
Julian Wruk ◽  
Kevin Cibis ◽  
Matthias Resch ◽  
Hanne Sæle ◽  
Markus Zdrallek
Keyword(s):  
Genetic Algorithm ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Network Planning ◽  
Voltage Regulation ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
The Impact

This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.

Associated Gas Utilization Using a Reheat Gas Turbine – Part 1: The Impact of Engine Degradation on the Optimized Power, Energy, and Revenue From Sold Electricity

2021 ◽  
Author(s):  
Mafel Obhuo ◽  
Duabari S. Aziaka ◽  
Dodeye I. Igbong ◽  
Ibirabo M. Obhuo
Keyword(s):  
Genetic Algorithm ◽  
Total Energy ◽  
Gas Turbine ◽  
Similar Sequence ◽  
Associated Gas ◽  
Gas Utilization ◽  
Energy Degradation ◽  
Fuel Resource ◽  
The Impact

Abstract This study presents a methodology for optimizing the power from a fleet of engines that use associated gas as fuel. The effects of engine degradation on optimized power, energy, and electricity revenue have been evaluated. The Cranfield University TURBOMATCH has been used to simulate a 296MW reheat gas turbine. Four scenarios were considered — clean, optimistic, medium, and pessimistic. Genetic algorithm was used in optimizing the power generated from the fleets. In the sequence of clean, optimistic, medium, and pessimistic fleets, the optimization results show that the total optimized power values are 7324.6, 7245.1, 7164.0, and 7074.4MW respectively. In the same sequence, the total energy generated is 64.2, 63.5, 62.8, and 61.9 billion kWh. In a similar sequence still, the electricity revenue is 8.487, 8.390, 8.298, and 8.192 billion US dollars respectively. In comparison with the clean, engine degradation resulted in a 1.09%, 2.19%, and 3.42% decrease in energy for the optimistic, medium, and pessimistic degraded fleets respectively. In the same sequence as the decrease in energy, degradation resulted in a 1.15%, 2.23%, and 3.48% decrease in electricity revenue. The methodology and results presented in this paper would serve as a guide for associated gas investors in the economic utilization of this fuel resource. This is innovative; it has not been done with the Alstom GT-26 engine.

Inflation shocks and income inequality

2019 ◽  
Vol 10 (2) ◽  
pp. 226-240
Author(s):  
Rolando Gonzales ◽  
Andrea Rojas-Hosse
Keyword(s):  
Genetic Algorithm ◽  
Methodological Approach ◽  
Mena Region ◽  
Content Type ◽  
Core Inflation ◽  
Research Gap ◽  
Using Data ◽  
The Impact ◽  
The Relationship ◽  
Detrimental Impact

Purpose The purpose of this paper is to analyze the effects of inflationary shocks on inequality, using data of selected countries of the Middle East and North Africa (MENA). Design/methodology/approach Inflationary shocks were measured as deviations from core inflation, based on a genetic algorithm. Bayesian quantile regression was used to estimate the impact of inflationary shocks in different levels of inequality. Findings The results showed that inflationary shocks substantially affect countries with higher levels of inequality, thus suggesting that the detrimental impact of inflation is exacerbated by the high division of classes in a country. Originality/value The study contributes to the literature about the relationship between inflation and inequality by proposing that not only the sustained increase in prices but also the inflationary shocks – the deviations from core inflation – contribute to the generation of inequality. Also, to the best of the authors knowledge, the relationship between inflation shocks and inequality in the MENA region has never been analyzed before, thus creating a research gap to provide additional empirical evidence about the sources of inequality. Additionally, the authors contribute with a methodological approach to measure inflationary shocks, based on a semelparous genetic algorithm.

scholarly journals OPTIMASI PEMASANGAN KAPASITOR PADA SISTEM JARINGAN LISTRIK DISTRIBUSI DI BALI MENGGUNAKAN METODE QUANTUM GENETIC ALGORITHM

2019 ◽  
Vol 6 (1) ◽  
pp. 96
Author(s):  
Maria Gusti Agung Ayu Permata ◽  
Antonius Ibi Weking ◽  
Widyadi Setiawan
Keyword(s):  
Genetic Algorithm ◽  
Power Factor ◽  
Power Loss ◽  
Grid System ◽  
Distribution Grid ◽  
Quantum Genetic Algorithm ◽  
Medium Voltage ◽  
Genetic Algorithm Method ◽  
The Impact

This study optimized the installation of capacitors on the distribution grid system in Penyabangan feeder, Singaraja, Bali using the Quantum Genetic Algorithm method to reduce the impact of increasing reactive currents. This feeder, consisting of 162 buses in which 62 buses are directly connected to the load and 5 buses are connected to medium voltage cosumers. The results of this study are obtained by the capacity of capacitors installed in 31 buses that have a power factor value less than the permitted limit and the power loss values decrease from 0,0674 MW and 0,0546 MVAr to 0,0543 MW and 0,0442 MVAr.

scholarly journals Multiobjective Optimization Method for Polymer Injection Molding Based on a Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Zhijun Yuan ◽  
Hui Wang ◽  
Xuebing Wei ◽  
Kui Yan ◽  
Cheng Gao
Keyword(s):  
Genetic Algorithm ◽  
Injection Molding ◽  
Multiobjective Optimization ◽  
Optimization Method ◽  
Optimal Combination ◽  
Cover Plate ◽  
Polymer Injection ◽  
Impact Parameters ◽  
Sink Marks ◽  
The Impact

To solve the quality problem of polymer injection parts, a quality prediction and multiobjective optimization method is established. In this method, the parameters that have an important effect on the part quality are selected using an orthogonal testing method, and then a central composite design experiment is performed using these parameters. A mathematical model considering an objective and impact factors is developed using the response surface method. The optimal combination of the impact parameters is determined using a multiobjective genetic algorithm. The injection molding of a typical interior trim part of a car, i.e., the seat belt cover plate, is used as an example to demonstrate the method. The two most troublesome problems in this process—the sink marks and warpage—are multiobjectively analyzed using the established method, and the optimal combination of impact parameters that minimized the defects is determined. The errors of the sink marks and warpage between the experimental and theoretical values were 7.95% and 0.2%, respectively. The optimized parameters were tested in actual injection molding. The results show that the shrinkage and warpage of the parts are obviously improved by optimization using the proposed method, allowing the parts to satisfy the requirements of assembly and appearance.

Hangzhou Housing Demand Forecasting Model Based on BP Neural Network of Genetic Algorithm Optimization

2014 ◽  
Vol 587-589 ◽  
pp. 37-41 ◽  
Author(s):  
Yi Hua Mao ◽  
Meng Bo Zhang ◽  
Ning Bo Yao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Real Estate ◽  
Housing Market ◽  
Bp Neural Network ◽  
Housing Demand ◽  
Forecasting Model ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
The Impact

Hangzhou, the capital of Zhejiang province and a famous scenic tourist city in China, goes at the forefront of the country for its high real estate prices, which hold a very important position of orientation to pricing in the real estate markets of the Yangtze River Delta region and of the whole country as well. The price trend of Hangzhou's real estate is even related to the sustainable development of the city. This paper uses the macro data on the housing market in Hangzhou during 1999-2012 to establish a forecasting model which is based on BP neural network of genetic algorithm optimization. With MATLAB software exploited for programming and simulation, the prediction made by the model about the housing demand in Hangzhou and the subsequent re-examination show that the model has high precision. But due to the impact of the national macro-control policies on housing market, the predictive value of some years may fluctuate to a certain extent.

HYPER-ELASTIC PARAMETER ESTIMATION OF HUMAN HEEL-PAD: A FINITE ELEMENT AND EVOLUTIONARY BASED ALGORITHM

2012 ◽  
Vol 12 (03) ◽  
pp. 1250034 ◽  
Author(s):  
M. M. KHANI ◽  
H. KATOOZIAN ◽  
K. AZMA ◽  
I. NASEH ◽  
A. H. SALIMI
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Finite Element ◽  
Nonlinear Material ◽  
Elastic Behavior ◽  
Diabetic Patients ◽  
Initial Contact ◽  
Heel Pad ◽  
Hyper Elastic ◽  
The Impact

The heel-pad as a biological shock absorber has an important role in the initial contact phase of gait cycle dissipating the impact forces resulted in locomotion. An axisymmetric finite element model of human heel-pad has been generated and the heel-pad experimental data deduced from a published force-deflection graph of the same specimen (Iain R. Spears, Janice E. Miller-Young), Iterative identification task has been used to extract nonlinear material properties describing hyper-elastic behavior of heel-pad. The genetic algorithm was incorporated into estimation process using an interface program. Two parameters of hyper-elastic materials potential energy function represented by Mooney–Rivlin were determined by using the genetic algorithm technique to minimize the displacement error between the experimental data and the corresponding finite element results after a considerable number of iterations. The result can be used for design and construction of synthetic heel-pad and therapeutic foot wear as well as insoles, especially for diabetic patients.

Newton’s Law of Universal Gravitation Based Genetic Algorithm for Graph Distribution

2016 ◽  
Vol 15 (04) ◽  
pp. 1650022
Author(s):  
Tabib Nacer ◽  
Saidouni Djamel Eddine
Keyword(s):  
Genetic Algorithm ◽  
Comparative Studies ◽  
Fitness Function ◽  
The Novel ◽  
Distribution Problem ◽  
Universal Gravitation ◽  
Workload Balancing ◽  
The Third ◽  
Heterogeneous Architectures ◽  
The Impact

This paper proposes a novel versatile genetic algorithm (GA) for solving the graph distribution problem. The new GA is based mainly on an inspirational idea that exploits the Newton’s universal gravitation law to introduce a novel GA fitness function. By this,the new GA preserves the workload balancing property between the different sites of the graph network and reduces the inter-processors communications overhead. Moreover, three main variants of the novel GA are developed. The two first; centralized and distributed variants, are developed to conduct a graph distribution over homogeneous architectures. The third variant is a distributed one devoted for heterogeneous architectures where the impact of the auto-adaptation features of the GA emerges. The results obtained and the comparative studies show the effectiveness of the proposed methods.

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