scholarly journals Hitting times of local and global optima in genetic algorithms with very high selection pressure

2017 ◽  
Vol 27 (3) ◽  
pp. 323-339 ◽  
Author(s):  
Anton Eremeev
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Selection Pressure ◽  
Upper Bounds ◽  
Hitting Times ◽  
Global Optima ◽  
High Selection ◽  
First Hitting Times ◽  
Very High

The paper is devoted to upper bounds on the expected first hitting times of the sets of local or global optima for non-elitist genetic algorithms with very high selection pressure. The results of this paper extend the range of situations where the upper bounds on the expected runtime are known for genetic algorithms and apply, in particular, to the Canonical Genetic Algorithm. The obtained bounds do not require the probability of fitness-decreasing mutation to be bounded by a constant which is less than one.

Growth and Seed Return of Auxin-Type Herbicide Resistant Wild Mustard (Brassica kaber) in Wheat

Weed Science ◽  
1996 ◽  
Vol 44 (4) ◽  
pp. 871-878 ◽  
Author(s):  
Daniel J. Debreuil ◽  
Lyle F. Friesen ◽  
Ian N. Morrison
Keyword(s):  
Selection Pressure ◽  
Dry Matter ◽  
Dry Matter Accumulation ◽  
Recommended Dosage ◽  
Herbicide Resistant ◽  
Wild Mustard ◽  
Phenoxy Herbicide ◽  
High Selection ◽  
Brassica Kaber ◽  
Very High

The growth and seed return of auxin herbicide resistant (R) wild mustard was compared to that of a susceptible (S) biotype in wheat in the field. In the absence of herbicide, the S biotype accumulated shoot dry matter more quickly than the R biotype throughout most of the growing season. However, in only one of the two years did the S biotype set substantially more seed than the R biotype (3120 versus 2520 seeds plant−1). The recommended dosage of 2,4-D for wild mustard control (420 g ai ha−1) killed all S plants in both years of the study, and severely inhibited growth and seed return of R plants. Shoot dry matter accumulation and seed return of treated R plants were reduced 75 to 90% compared to the untreated control. However, at a density of 20 plants m−2R seed return was still very high; 9000 and 5700 seeds m−2in 1992 and 1993, respectively. The recommended dosage of dicamba (300 g ha−1) did not inhibit the growth and seed return of either S or R wild mustard to the same extent as 2,4-D. Dicamba at 300 g ha−1reduced S shoot dry matter and seed return 80 to 90%, while R shoot dry matter and seed return was reduced 60 to 65%. The results of this study indicate a very high selection pressure for R wild mustard at recommended dosages of 2,4-D. Despite a high selection pressure, and considering the long history of phenoxy herbicide usage on the Prairies, the relatively rare occurrence of phenoxy herbicide resistant weeds implies that the frequency of resistant individuals is very low. From a mathematical model it was determined that the frequency of R wild mustard in an unselected population may be in the order of 10−30.

Formulation of Colors Using a Genetic Algorithm

2012 ◽  
Vol 17 (4) ◽  
pp. 241-244
Author(s):  
Cezary Draus ◽  
Grzegorz Nowak ◽  
Maciej Nowak ◽  
Marcin Tokarski
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Production Process ◽  
Computer Technology ◽  
Manual Method ◽  
Apply Genetic ◽  
Computer Aided ◽  
Multiple Samples ◽  
Plastics Industry

Abstract The possibility to obtain a desired color of the product and to ensure its repeatability in the production process is highly desired in many industries such as printing, automobile, dyeing, textile, cosmetics or plastics industry. So far, most companies have traditionally used the "manual" method, relying on intuition and experience of a colorist. However, the manual preparation of multiple samples and their correction can be very time consuming and expensive. The computer technology has allowed the development of software to support the process of matching colors. Nowadays, formulation of colors is done with appropriate equipment (colorimeters, spectrophotometers, computers) and dedicated software. Computer-aided formulation is much faster and cheaper than manual formulation, because fewer corrective iterations have to be carried out, to achieve the desired result. Moreover, the colors are analyzed with regard to the metamerism, and the best recipe can be chosen, according to the specific criteria (price, quantity, availability). Optimaization problem of color formulation can be solved in many diferent ways. Authors decided to apply genetic algorithms in this domain.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals Multimodal Optimization of Permutation Flow-Shop Scheduling Problems Using A Clustering-Genetic-Algorithm-Based Approach

2021 ◽  
Vol 11 (8) ◽  
pp. 3388
Author(s):  
Pan Zou ◽  
Manik Rajora ◽  
Steven Y. Liang
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Flow Shop ◽  
Optimal Schedule ◽  
Flow Shop Scheduling ◽  
Multimodal Optimization ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop ◽  
Global Optima

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimodal optimization of PFSSP. In the proposed algorithm, the k-means clustering algorithm is first utilized to cluster the individuals of every generation into different clusters, based on some machine-sequence-related features. Next, the operators of GA are applied to the individuals belonging to the same cluster to find multiple global optima. Unlike standard GA, where all individuals belong to the same cluster, in the proposed approach, these are split into multiple clusters and the crossover operator is restricted to the individuals belonging to the same cluster. Doing so, enabled the proposed algorithm to potentially find multiple global optima in each cluster. The performance of the proposed algorithm was evaluated by its application to the multimodal optimization of benchmark PFSSP. The results obtained were also compared to the results obtained when other niching techniques such as clearing method, sharing fitness, and a hybrid of the proposed approach and sharing fitness were used. The results of the case studies showed that the proposed algorithm was able to consistently converge to better optimal solutions than the other three algorithms.

Minimization of total trim loss occurring in pipe cutting in shipbuilding with genetic algorithm

2021 ◽  
pp. 147509022199169
Author(s):  
Abdullah Türk ◽  
Dursun Saral ◽  
Murat Özkök ◽  
Ercan Köse
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Search Space ◽  
Cutting Process ◽  
Genetic Operators ◽  
Critical Stage ◽  
Different Types ◽  
Trim Loss ◽  
Pipe Systems

Outfitting is a critical stage in the shipbuilding process. Within the outfitting, the construction of pipe systems is a phase that has a significant effect on time and cost. While cutting the pipes required for the pipe systems in shipyards, the cutting process is usually performed randomly. This can result in large amounts of trim losses. In this paper, we present an approach to minimize these losses. With the proposed method it is aimed to base the pipe cutting process on a specific systematic. To solve this problem, Genetic Algorithms (GA), which gives successful results in solving many problems in the literature, have been used. Different types of genetic operators have been used to investigate the search space of the problem well. The results obtained have proven the effectiveness of the proposed approach.

Carbon Brake Friction Model Parameter Identification Using Genetic Algorithms

1995 ◽  
Author(s):  
Roger C. von Doenhoff ◽  
Robert J. Streifel ◽  
Robert J. Marks
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Surface Temperature ◽  
Parameter Identification ◽  
Friction Model ◽  
Carbon Surface ◽  
Model Parameters ◽  
Model Parameter ◽  
Brake Pressure ◽  
Model Parameter Identification

Abstract A model of the friction characteristics of carbon brakes is proposed to aid in the understanding of the causes of brake vibration. The model parameters are determined by a genetic algorithm in an attempt to identify differences in friction properties between brake applications during which vibration occurs and those during which there is no vibration. The model computes the brake torque as a function of wheelspeed, brake pressure, and the carbon surface temperature. The surface temperature is computed using a five node temperature model. The genetic algorithm chooses the model parameters to minimize the error between the model output and the torque measured during a dynamometer test. The basics of genetic algorithms and results of the model parameter identification process are presented.

The Effect of Temperature Dependent Yield Strength on Upper Bounds for Creep Ratcheting

2006 ◽  
Author(s):  
Timothy E. McGreevy ◽  
Frederick A. Leckie ◽  
Peter Carter ◽  
Douglas L. Marriott
Keyword(s):  
Yield Strength ◽  
Upper Bounds ◽  
Effect Of Temperature ◽  
Strain Accumulation ◽  
Core Concept ◽  
Temperature Dependent ◽  
Inelastic Analysis ◽  
Asme Code ◽  
Inelastic Design ◽  
Very High

The Bree model and the elastic core concept have been used as the foundation for the simplified inelastic design analysis methods in the ASME Code for the design of components at elevated temperature for nearly three decades. The methodology provides upper bounds for creep strain accumulation and a physical basis for ascertaining if a structure under primary and secondary loading will behave elastically, plastically, shakedown, or ratchet. Comparisons of the method with inelastic analysis results have demonstrated its conservatism in stainless steel at temperatures representative of those in LMBR applications. The upper bounds on creep accumulation are revisited for very high temperatures representative of VHTR applications, where the yield strength of the material is strongly dependent upon temperature. The effect of the variation in yield strength on the evolution of the core stress is illustrated, and is shown to extend the shakedown regions, and affects the location of the boundaries between shakedown, ratcheting, and plasticity.

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