نتایج جستجو برای: multiple crossover and mutation operator
تعداد نتایج: 16968324 فیلتر نتایج به سال:
The traditional crossover operator used in genetic search exhibits a position-dependent bias called the dcfining-length bias. We show how this bias results in hidden biases that are difficult to anticipate and compensate for. We introduce a new crossover operator, shuffle crossover, that eliminates the position dependent bias of the traditional crossover operator by shuffling the representation...
The performance of Genetic Algorithm (GA) depends on various operators. Crossover operator is one of them. Crossover operators are mainly classified as application dependent crossover operators and application independent crossover operators. Effect of crossover operators in GA is application as well as encoding dependent. This paper will help researchers in selecting appropriate crossover oper...
This paper analyzes the convergence properties of the canonical genetic algorithm (CGA) with mutation, crossover and proportional reproduction applied to static optimization problems. It is proved by means of homogeneous finite Markov chain analysis that a CGA will never converge to the global optimum regardless of the initialization, crossover, operator and objective function. But variants of ...
In this paper we propose a robust genetic algorithm for the multi-mode resourceconstrained project scheduling problem. We present a new representation for the solutions to this problem, which includes an additional bit indicating the scheduling scheme used to construct the schedule, forward or backward. Moreover, we have developed new genetic operators which operate over this representation, ex...
Minimum vertex cover problem (MVCP) is an NP-hard problem and it has numerous real life applications. This paper presents hybrid genetic algorithm (HGA) to solve MVCP efficiently. In this paper we have demonstrated that when local optimization technique is added to genetic algorithm to form HGA, it gives near to optimal solution speedy. We have developed new heuristic vertex crossover operator ...
Application of artificial neural networks (ANN) in areas such as classification of images and audio signals shows the ability of this artificial intelligence technique for solving practical problems. Construction and training of ANNs is usually a time-consuming and hard process. A suitable neural model must be able to learn the training data and also have the generalization ability. In this pap...
This paper studies the parallel machine scheduling problem subject to machine and job deterioration in a batched delivery system. By the machine deterioration effect, we mean that each machine deteriorates over time, at a different rate. Moreover, job processing times are increasing functions of their starting times and follow a simple linear deterioration. The objective functions are minimizin...
We compare the search power of crossover and mutation in Genetic Algorithms. Our discussion is framed within a model of computation using search space structures induced by these operators. Iso-morphisms between the search spaces generated by these operators on small populations are identiied and explored. These are closely related to the binary reeected Gray code. Using these we generate discr...
در این پایان نامه مرکز ساز جبرهای عملگری استاندارد وh^*- جبرهای نیم ساده را بیان می کنیم. فرض کنیم a یک *h-جبر نیم ساده و t: a -> a یک نگاشت جمعی باشد به طوری که به ازای هر x∈a و بعضی n ≥ 1 داشته باشیم. 2t(x n+1) = t(x)xn + xnt(x) در این صورت t یک مرکزساز چپ و راست است. این پایان نامه بر اساس مقاله ی زیر نوشته شده است: i. kosi-ulbl and j. vukman, on centralizers of standard operator algebras ...
The selection operator in the standard genetic algorithm (GA) determines which individuals are chosen from a relatively homologous population for mating and crossover. This operator is crucial for the performance of the GA, since it may lead the algorithm to premature convergence and limited search scope (or genetic diversity) by repeatedly choosing very strong individuals with similar genetic ...
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