scholarly journals Improving the Performance of Multiobjective Genetic Algorithms: An Elitism-Based Approach

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 587
Author(s):  
Giorgio Guariso ◽  
Matteo Sangiorgio
Keyword(s):  
Genetic Algorithms ◽  
Fitness Function ◽  
Pareto Frontier ◽  
Optimal Solution ◽  
Variable Number ◽  
Initial Population ◽  
Multiobjective Genetic Algorithms ◽  
Multiobjective Problems ◽  
Rough Approximation ◽  
Successive Generations

Today, many complex multiobjective problems are dealt with using genetic algorithms (GAs). They apply the evolution mechanism of a natural population to a “numerical” population of solutions to optimize a fitness function. GA implementations must find a compromise between the breath of the search (to avoid being trapped into local minima) and its depth (to prevent a rough approximation of the optimal solution). Most algorithms use “elitism”, which allows preserving some of the current best solutions in the successive generations. If the initial population is randomly selected, as in many GA packages, the elite may concentrate in a limited part of the Pareto frontier preventing its complete spanning. A full view of the frontier is possible if one, first, solves the single-objective problems that correspond to the extremes of the Pareto boundary, and then uses such solutions as elite members of the initial population. The paper compares this approach with more conventional initializations by using some classical tests with a variable number of objectives and known analytical solutions. Then we show the results of the proposed algorithm in the optimization of a real-world system, contrasting its performances with those of standard packages.

scholarly journals Analysis of Selection Methods Used in Genetic Algorithms

2021 ◽  
Vol 4 ◽  
pp. 29-43
Author(s):  
Nataliya Gulayeva ◽  
Artem Ustilov
Keyword(s):  
Growth Rate ◽  
Genetic Algorithms ◽  
Selection Pressure ◽  
Fitness Function ◽  
Selection Method ◽  
Initial Population ◽  
Reproduction Rate ◽  
Selection Methods ◽  
Selection Scheme ◽  
Fitness Value

This paper offers a comprehensive review of selection methods used in the generational genetic algorithms.Firstly, a brief description of the following selection methods is presented: fitness proportionate selection methods including roulette-wheel selection (RWS) and its modifications, stochastic remainder selection with replacement (SRSWR), remainder stochastic independent selection (RSIS), and stochastic universal selection (SUS); ranking selection methods including linear and nonlinear rankings; tournament selection methods including deterministic and stochastic tournaments as well as tournaments with and without replacement; elitist and truncation selection methods; fitness uniform selection scheme (FUSS).Second, basic theoretical statements on selection method properties are given. Particularly, the selection noise, selection pressure, growth rate, reproduction rate, and computational complexity are considered. To illustrate selection method properties, numerous runs of genetic algorithms using the only selection method and no other genetic operator are conducted, and numerical characteristics of analyzed properties are computed. Specifically, to estimate the selection pressure, the takeover time and selection intensity are computed; to estimate the growth rate, the ratio of best individual copies in two consecutive populations is computed; to estimate the selection noise, the algorithm convergence speed is analyzed based on experiments carried out on a specific fitness function assigning the same fitness value to all individuals.Third, the effect of selection methods on the population fitness distribution is investigated. To do this, there are conducted genetic algorithm runs starting with a binomially distributed initial population. It is shown that most selection methods keep the distribution close to the original one providing an increased mean value of the distribution, while others (such as disruptive RWS, exponential ranking, truncation, and FUSS) change the distribution significantly. The obtained results are illustrated with the help of tables and histograms.

A Natural Evolution Based Numerical Optimisation Framework to Develop and Enhance Airfoil-Slat Arrangement

2019 ◽  
Author(s):  
Sushrut Kumar ◽  
Priyam Gupta ◽  
Raj Kumar Singh
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Fuel Efficiency ◽  
Optimal Solution ◽  
Leading Edge ◽  
Initial Population ◽  
Invasive Weed ◽  
Fitness Value ◽  
Leading Edge Slats ◽  
Dynamics Simulations

Abstract Leading Edge Slats are popularly being put into practice due to their capability to provide a significant increase in the lift generated by the wing airfoil and decrease in the stall. Consequently, their optimum design is critical for increased fuel efficiency and minimized environmental impact. This paper attempts to develop and optimize the Leading-Edge Slat geometry and its orientation with respect to airfoil using Genetic Algorithm. The class of Genetic Algorithm implemented was Invasive Weed Optimization as it showed significant potential in converging design to an optimal solution. For the study, Clark Y was taken as test airfoil. Slats being aerodynamic devices require smooth contoured surfaces without any sharp deformities and accordingly Bézier airfoil parameterization method was used. The design process was initiated by producing an initial population of various profiles (chromosomes). These chromosomes are composed of genes which define and control the shape and orientation of the slat. Control points, Airfoil-Slat offset and relative chord angle were taken as genes for the framework and different profiles were acquired by randomly modifying the genes within a decided design space. To compare individual chromosomes and to evaluate their feasibility, the fitness function was determined using Computational Fluid Dynamics simulations conducted on OpenFOAM. The lift force at a constant angle of attack (AOA) was taken as fitness value. It was assigned to each chromosome and the process was then repeated in a loop for different profiles and the fittest wing slat arrangement was obtained which had an increase in CL by 78% and the stall angle improved to 22°. The framework was found capable of optimizing multi-element airfoil arrangements.

scholarly journals A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Optimal Solution ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Adaptive Mutation ◽  
Local Optima ◽  
3 Dimensional ◽  
Successive Generations

Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals Using computation to enhance diagnosis and therapy: a novel mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Novel Mutation ◽  
Fitness Function ◽  
Genetic Disorders ◽  
Optimal Solution ◽  
Global Optimum ◽  
Adaptive Mutation ◽  
Local Optima

The fields of molecular biology and neurobiology have advanced rapidly over the last two decades. These advances have resulted in the development of large proteomic and genetic databases that need to be searched for the prediction, early detection and treatment of neuropathologies and other genetic disorders. This need, in turn, has pushed the development of novel computational algorithms that are critical for searching genetic databases. One successful approach has been to use artificial intelligence and pattern recognition algorithms, such as neural networks and optimization algorithms (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate based on the fitness function of passing generations. We propose a novel pseudo-derivative based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals Building a Computer Vocational Guidance System for Graduates of Secondary Educational Institutions Based on a Genetic Algorithm

2020 ◽  
Vol 24 (3) ◽  
pp. 33-43
Author(s):  
A. P. Sergushicheva ◽  
E. N. Davydova
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Career Guidance ◽  
Optimization Problems ◽  
Fitness Function ◽  
Vocational Guidance ◽  
Guidance System ◽  
Educational Institutions ◽  
Initial Population

The purpose of the article is to present the results of a study on the development of a genetic algorithm to solve the problems of career guidance for graduates of secondary educational institutions and to verify the possibility of its implementation in a computer system. The issue of career guidance for graduates is still relevant, problematic and not fully resolved. According to the authors, the introduction of artificial intelligence technologies in career guidance systems is a promising area that should be paid attention to. Genetic algorithms are widely used to solve search and optimization problems in various subject areas. The authors propose to automate the process of identifying the tendency of secondary school graduates to a particular type of activity by building a vocational guidance system based on a genetic algorithm.Materials and methods. To identify an individual’s predisposition to a specific type of activity, it is necessary to have a list of requirements and contraindications to the profession. Among the ways of describing the norms and requirements for the applicant-specialist are professiograms, lists of necessary competencies and others. To determine the characteristics of the individual that affect the choice of profession, it is possible to use special tests, activating questionnaires, grades in school subjects. The authors carry out the comparison of personality characteristics and requirements through a genetic algorithm. Genetic algorithms belong to the group of evolutionary methods and are based on the evolutionary theory. Among their advantages are conceptual simplicity and wide applicability, resistance to dynamic environmental changes and the ability to self-organize.Results. The genetic algorithm has been developed, in which as a source of information for creating a new population individual certificate evaluations are accepted. Based on these estimates, an initial population of professions is formed. As a result of crossing a pair of individuals from the parent population, a descendant is obtained whose chromosome consists of the genes of both parents. The selection of surviving specimens is based on the percentage of success in the development of each of the professions in the list and the fitness function. The developed algorithm was implemented in a software system. As experiments showed, the genetic algorithm successfully copes with the task of finding the optimal list of professions according to a given criterion.Conclusion. The results of the study show that the use of genetic algorithms provides convenient mechanisms for introducing artificial intelligence methods into the field of career guidance, which improves the quality of recommendations for choosing a profession.

Using Case-Based Reasoning and Soft Computing Techniques for the Initialization of Engineering Design Optimization

2006 ◽  
Author(s):  
K. M. Saridakis ◽  
A. J. Dentsoras
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Initial Conditions ◽  
Initial Point ◽  
Optimal Solution ◽  
Pattern Search ◽  
Design Parameters ◽  
Initial Population ◽  
Case Based Reasoning ◽  
Case Based

From a certain point of view, parametric engineering design may be considered as an optimization problem. The design problem may be represented through a set of design parameters. The optimal solution is located by using a set of competing design parameters and its evaluation is based upon specific criteria. A significant number of techniques and methodologies have been proposed in order to perform this difficult task. The selection of the appropriate one(s) depends strongly upon the nature and the specific characteristics of the design problem under consideration. The majority of these techniques and methodologies rely on the definition of some initial conditions. Wrong, misleading or incomplete initial conditions may result to solutions characterized by local optimality or may need excessive computational time in order to converge to either an optimal or a sub-optimal solution. In the context of the current work, two different approaches are used for initializing the optimization process: genetic algorithms and pattern search. Genetic algorithms need an initial population of individual solutions before the genetic operations could be deployed, while the pattern search techniques use a starting (initial) point for the optimization process. These two initial conditions (initial population and initial point) may be defined either randomly or deliberately. The present paper introduces a case-based design (CBD) module as pre-processor to the design optimization. This CBD module is based on an artificial competitive neural network, which is submitted to unsupervised learning by examples based on past design solutions. The new design is represented through fuzzy preferences and weighting factors, which are compiled by the neural network for retrieving similar past solutions. The retrieved solutions are used in order to determine the initial conditions of the optimization method (the initial population for the genetic algorithm (GA) or the starting point for the pattern search). The optimal solution is then searched using the criterion of the maximum aggregated overall preference. A system, namely Case-DeSC, has been developed in the purpose of evaluating the proposed framework in the application area of parametric design of oscillating conveyors. The results show that the proposed optimization methods converge faster to more efficient solutions if case-based reasoning (CBR) is utilized for defining the initial optimization conditions.

scholarly journals Algebraic Solution to Constrained Bi-Criteria Decision Problem of Rating Alternatives through Pairwise Comparisons

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 303
Author(s):  
Nikolai Krivulin
Keyword(s):  
Decision Problem ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Sufficient Conditions ◽  
Approximation Error ◽  
Optimal Solution ◽  
Algebraic Solution ◽  
Pairwise Comparisons ◽  
Logarithmic Scale ◽  
Idempotent Algebra

We consider a decision-making problem to evaluate absolute ratings of alternatives from the results of their pairwise comparisons according to two criteria, subject to constraints on the ratings. We formulate the problem as a bi-objective optimization problem of constrained matrix approximation in the Chebyshev sense in logarithmic scale. The problem is to approximate the pairwise comparison matrices for each criterion simultaneously by a common consistent matrix of unit rank, which determines the vector of ratings. We represent and solve the optimization problem in the framework of tropical (idempotent) algebra, which deals with the theory and applications of idempotent semirings and semifields. The solution involves the introduction of two parameters that represent the minimum values of approximation error for each matrix and thereby describe the Pareto frontier for the bi-objective problem. The optimization problem then reduces to a parametrized vector inequality. The necessary and sufficient conditions for solutions of the inequality serve to derive the Pareto frontier for the problem. All solutions of the inequality, which correspond to the Pareto frontier, are taken as a complete Pareto-optimal solution to the problem. We apply these results to the decision problem of interest and present illustrative examples.

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 Adaptive Gene Level Mutation

Algorithms ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 16
Author(s):  
Jalal Al-afandi ◽  
Horváth András
Keyword(s):  
Genetic Algorithms ◽  
Stochastic Optimization ◽  
Specific Problem ◽  
Traditional Approach ◽  
Fitness Function ◽  
Optimization Methods ◽  
Analytic Solutions ◽  
Problem Representation ◽  
Travelling Salesman ◽  
Gene Level

Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.

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