scholarly journals ABOUT FEATURES OF MUTATION APPLICATION IN A MODIFIED OPERATOR GENETIC ALGORITHM

Author(s):  
Leonid Oliinyk ◽  
Stanislav Bazhan

Genetic algorithm is a method of optimization based on the concepts of natural selection and genetics. Genetic algorithms are used in software development, in artificial intelligence systems, a wide range of optimization problems and in other fields of knowledge.One of the important issues in the theory of genetic algorithms and their modified versions is the search for the best balance between performance and accuracy. The most difficult in this sense are problems where the fitness function in the search field has many local extremes and one global or several global extremes that coincide.The effectiveness of the genetic algorithm depends on various factors, such as the successful creation of the primary population. Also in the theory of genetic algorithms, recombination methods play an important role to obtain a better population of offspring. The aim of this work is to study some types of mutations using a modified genetic algorithm to find the minimum function of one variable.The article presents the results of research and analysis of the impact of some mutation procedures. Namely, the effect of mutation on the speed of achieving the solution of the problem of finding the global extremum of a function of one variable. For which a modified genetic algorithm is used, where the operators of the "generalized crossover" are stochastic matrices

Author(s):  
Thomas Bäck

This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.


2016 ◽  
Vol 25 (3) ◽  
pp. 319-333 ◽  
Author(s):  
Amanda House ◽  
Arvin Agah

AbstractThis paper applies a genetic algorithm (GA) to the autonomous evolution of digital art, eliminating the need for a human in the loop. Creative applications of GAs face the challenge of producing art or music to fit a wide range of human tastes. One approach is to use a human in the loop to determine the fitness function in order to direct the selection and evolution. Another approach, which this paper explores, is to define an objective fitness function to automate evolution without the need for human input. In this paper, several features of digital art are identified and used as the basis for fitness functions. The resulting images are recognizable for the intended evolution of the fitness function used. This indicates the potential of an approach to create more robust algorithmic fitness functions capable of evolving creative applications autonomously.


Author(s):  
N. M. Gulayeva ◽  
S. A. Yaremko

Context. Niching genetic algorithms are one of the most popular approaches to solve multimodal optimization problems. When classifying niching genetic algorithms it is possible to select algorithms explicitly analyzing topography of fitness function landscape; multinational genetic algorithm is one of the earliest examples of these algorithms. Objective. Development and analysis of the multinational genetic algorithm and its modifications to find all maxima of a multimodal function. Method. Experimental analysis of algorithms is carried out. Numerous runs of algorithms on well-known test problems are conducted and performance criteria are computed, namely, the percentage of convergence, real (global, local) and fake peak ratios; note that peak rations are computed only in case of algorithm convergence. Results. Software implementation of a multinational genetic algorithm has been developed and experimental tuning of its parameters has been carried out. Two modifications of hill-valley function used for determining the relative position of individuals have been proposed. Experimental analysis of the multinational genetic algorithm with classic hill-valley function and with its modifications has been carried out. Conclusions. The scientific novelty of the study is that hill-valley function modifications producing less number of wrong identifications of basins of attraction in comparison with classic hill-valley function are proposed. Using these modifications yields to performance improvements of the multinational genetic algorithm for a number of test functions; for other test functions improvement of the quality criteria is accompanied by the decrease of the convergence percentage. In general, the convergence percentage and the quality criterion values demonstrated by the algorithm studied are insufficient for practical use in comparison with other known algorithms. At the same time using modified hill-valley functions as a post-processing step for other niching algorithms seems to be a promising improvement of performance of these algorithms.


2020 ◽  
Vol 24 (3) ◽  
pp. 33-43
Author(s):  
A. P. Sergushicheva ◽  
E. N. Davydova

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.


Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak

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.


2018 ◽  
Vol 26 (2) ◽  
pp. 237-267 ◽  
Author(s):  
Chao Qian ◽  
Yang Yu ◽  
Ke Tang ◽  
Yaochu Jin ◽  
Xin Yao ◽  
...  

In real-world optimization tasks, the objective (i.e., fitness) function evaluation is often disturbed by noise due to a wide range of uncertainties. Evolutionary algorithms are often employed in noisy optimization, where reducing the negative effect of noise is a crucial issue. Sampling is a popular strategy for dealing with noise: to estimate the fitness of a solution, it evaluates the fitness multiple ([Formula: see text]) times independently and then uses the sample average to approximate the true fitness. Obviously, sampling can make the fitness estimation closer to the true value, but also increases the estimation cost. Previous studies mainly focused on empirical analysis and design of efficient sampling strategies, while the impact of sampling is unclear from a theoretical viewpoint. In this article, we show that sampling can speed up noisy evolutionary optimization exponentially via rigorous running time analysis. For the (1[Formula: see text]1)-EA solving the OneMax and the LeadingOnes problems under prior (e.g., one-bit) or posterior (e.g., additive Gaussian) noise, we prove that, under a high noise level, the running time can be reduced from exponential to polynomial by sampling. The analysis also shows that a gap of one on the value of [Formula: see text] for sampling can lead to an exponential difference on the expected running time, cautioning for a careful selection of [Formula: see text]. We further prove by using two illustrative examples that sampling can be more effective for noise handling than parent populations and threshold selection, two strategies that have shown to be robust to noise. Finally, we also show that sampling can be ineffective when noise does not bring a negative impact.


2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.


2021 ◽  
Author(s):  
Rekha G ◽  
Krishna Reddy V ◽  
chandrashekar jatoth ◽  
Ugo Fiore

Abstract Class imbalance problems have attracted the research community but a few works have focused on feature selection with imbalanced datasets. To handle class imbalance problems, we developed a novel fitness function for feature selection using the chaotic salp swarm optimization algorithm, an efficient meta-heuristic optimization algorithm that has been successfully used in a wide range of optimization problems. This paper proposes an Adaboost algorithm with chaotic salp swarm optimization. The most discriminating features are selected using salp swarm optimization and Adaboost classifiers are thereafter trained on the features selected. Experiments show the ability of the proposed technique to find the optimal features with performance maximization of Adaboost.


Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.


2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Tng C. H. John ◽  
Edmond C. Prakash ◽  
Narendra S. Chaudhari

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.


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