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Finite element-based structural optimization of assemblies found in ship hulls
Published in C. Guedes Soares, T.A. Santos, Trends in Maritime Technology and Engineering Volume 1, 2022
G. Giannopoulos, K.N. Anyfantis
Different from conventional algorithms, GAs are based on population, in which each individual is evolved parallelly and the ultimate result is included in the last population. The population evolution depends on some genetic operators acted on the current population and is realized by the generation of a new population. Generally, the genetic operators are selection, crossover and mutation. The MOGA separately stores two different sets of solutions a current population and a tentative set of non-dominated solutions. The aforementioned genetic operators are applied to each solution of the current population, creating newly generated solutions that replace the former. The tentative set of non-dominated solutions is also updated by the generated solutions. That is, if a solution obtained by the genetic operations is not dominated by any other solutions in the newly generated current population and the tentative set of non-dominated solutions, this solution is added to the tentative set. Afterwards the solutions that are dominated by the new added one are removed from the tentative set. In this manner, the tentative set of non-dominated solutions is updated at every generation in the MOGA.
Mobile Couriers and the Grid
Published in Fadi Al-Turjman, Smart Grid in IoT-Enabled Spaces, 2020
The following is the list of the assumed GA components mapped to the aforementioned DC election problem: Gene is represented by a decimal value that can be between zero and the number of APs.Chromosome is a candidate solution to the given problem and is composed of several genes. It is also called individual and coded as a finite length vector of genes (represented in bit strings).Search space is the area in which the search is performed to find the solution among the chromosomes.Fitness value is the associated with each chromosome, and it represents quality of the candidate solution.Population consists of a number of chromosomes and is maintained within a specific search space.Generation is the current population in any stage of the whole searching process.Genetic operator is a variety of operations to be applied on a chromosome for producing better individuals (e.g., crossing over and mutation).
Introduction to Expert Systems
Published in Chris Nikolopoulos, Expert Systems, 1997
A genetic algorithm solves a problem as follows. An initial set of possible solutions is chosen at random and the solutions are encoded as finite strings of symbols. The encoded set of possible solutions is called a population. The genetic algorithm does not need extensive knowledge of the problem domain in order to perform an efficient search of the solution space. It only requires a function (the fitness function) to measure the fitness of a candidate solution. The closer an encoded string is to being a solution, the bigger its fitness value should be. The initial population is successively transformed to populations of greater and greater overall fitness until a convergence criterion is met. During evolution the population size usually remains constant. The basic genetic operators used to transform a population to a better one are: reproduction, crossover and mutation.
Diagnosis of contamination discharge state of porcelain insulators based on GA-CNN
Published in Connection Science, 2023
Kegeng Zhang, Jinyuan Liu, Junshuai Zhong, Yizhou Jing
In 1969, John Holland formulated the genetic algorithm computational model by simulating the biological evolution process of Darwin’s theory of biological evolution and genetic mechanism. The model uses the extinction law of the fittest to simulate the natural evolution process, encodes the parameters in the optimised object to acquire chromosomes, and then evolves the chromosomes through selection, crossover and mutation, and finally generates the chromosomes needed by the object. Implement dynamic optimisation. According to the different optimisation objects, there are also differences in the coding methods of genetic algorithms. Researchers study different optimisation objects and obtain genetic operators suitable for various problems. Different genetic operators and coding methods form different genetic algorithms. When applying the genetic algorithm, the coding operation is performed first, and the coding method has a great influence on the subsequent genetic operation, so the choice of coding is of great significance.
Sustainable multi-objective process planning in reconfigurable manufacturing environment: adapted new dynamic NSGA-II vs New NSGA-III
Published in International Journal of Production Research, 2022
Imen Khettabi, Lyes Benyoucef, Mohamed Amine Boutiche
In this study, three genetic operators namely crossover, mutation, and perturbation are used. Crossover: in genetic algorithms, it's the most crucial operator. When a random crossing site is selected, two chromosomes exchange chain segments to form new chromosomes known as offspring or children. An example of crossover procedures is shown Figure 2(a).Mutation: it requires altering one or more genes on a certain chromosome at random in order to produce new offspring or children. In our case, regardless of the size of the chromosome, just two columns are chosen at random, as shown in Figure 2(b). We can see that columns two and five have been chosen and should alter at random. The obtained offspring or child is still a feasible process plan that will be assessed utilising the sets of potential operations sequences and triplets.Perturbation: it entails randomly choosing a specific proportion of the population's chromosomes to be modified. Rather of utilising mutation, the chromosomes will be disturbed, as shown in Figure 2(c). As we can see, all of the chromosome genes are randomly disrupted in order to produce a new offspring or child. Still, the obtained chromosome corresponds to a feasible process plan.
An intelligent matching recommendation algorithm for a manufacturing capacity sharing platform with fairness concerns
Published in International Journal of Production Research, 2022
Lei Xie, Jianghua Zhang, Qingchun Meng, Yan Jin, Weibo Liu
Mutation as a genetic operator maintains genetic diversity from one generation to the next. In this operation, one or more gene values in a chromosome from its initial state may be changed in each run of mutation. It is also a critical type of operator to conduct a local search for GAs. Concerning the 2-dimensional chromosome, a powerful mutation is needed. Additionally, since the profit of each order from buyers and platform is counted, only the total demand is satisfied. Therefore, herein, an order-first mutation is introduced. The rationale behind the order-first mutation is to fix the gap between the sum of transaction volumes and demand. Therefore, if there is a demand gap for each buyer, the maximum or the random transaction volume will be enlarged to bridge the gap.