نتایج جستجو برای: fitness genetic algorithm operation research
تعداد نتایج: 2773685 فیلتر نتایج به سال:
In this paper, a new genetic algorithm (GA) is presented for solving the multi-mode resource-constrained project scheduling problem (MRCPSP) with minimization of project makespan as the objective subject to resource and precedence constraints. A random key and the related mode list (ML) representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSG...
A constrained version of the Big Bang-Big Crunch algorithm for the efficient solution of the optimal reservoir operation problems is proposed in this paper. Big Bang-Big Crunch (BB-BC) algorithm is a new meta-heuristic population-based algorithm that relies on one of the theories of the evolution of universe namely, the Big Bang and Big Crunch theory. An improved formulation of the algorithm na...
in this paper, a new continuous ant colony optimization (caco) algorithm is proposed for optimal reservoir operation. the paper presents a new method of determining and setting a complete set of control parameters for any given problem, saving the user from a tedious trial and error based approach to determine them. the paper also proposes an elitist strategy for caco algorithm where best solut...
An improved adaptive Genetic Algorithm was proposed, and the method was applied to the optimization process of tile image registration. This paper improved traditional Genetic Algorithm in three aspects. The probability of crossover and mutation was adjusted in a dynamic way according to the change of the fitness of individual during the evolutionary process and in different way when the evolut...
It is still unclear how an evolutionary algorithm (EA) searches a fitness landscape, and on what fitness landscapes a particular EA will do well. The validity of the building-block hypothesis, a major tenet of traditional genetic algorithm theory, remains controversial despite its continued use to justify claims about EAs. This paper outlines a research program to begin to answer some of these ...
Evolutionary Computing techniques use an explicit fitness function or simulated to derive a solution to a problem from a population of individuals, over a number of generations. The general approach which allows such techniques to be used on problems in which evaluations are so costly, which cannot be expressed formally, or which are difficult to simulate, is examined [1]. Much work has been do...
We introduce a new parameter to discuss the behavior of a genetic algorithm. This parameter is the mean number of exact copies of the best fit chromosomes from one generation to the next. We argue that the genetic algorithm should operate efficiently when this parameter is slightly larger than 1. We consider the case of the simple genetic algorithm with the roulette–wheel selection mechanism. W...
Imperialist Competitive Algorithm (ICA) is considered as a prime meta-heuristic algorithm to find the general optimal solution in optimization problems. This paper presents a use of ICA for automatic clustering of huge unlabeled data sets. By using proper structure for each of the chromosomes and the ICA, at run time, the suggested method (ACICA) finds the optimum number of clusters while optim...
For many years, cryptanalysis has been considered as an attractive topic in jeopardizing the security and resistance of an encryption algorithm. The SDES encryption algorithm is a symmetric cryptography algorithm that performs a cryptographic operation using a crypt key. In the world of encryption, there are many search algorithms to cryptanalysis. In these researches, brute force attack algori...
Genetic Algorithms are an effective way to solve optimisation problems. If the fitness test takes a long time to perform then the Genetic Algorithm may take a long time to execute. Using conventional fitness functions Approximately a third of the time may be spent testing individuals that have already been tested. Intelligent Fitness Functions can be applied to improve the efficiency of the Gen...
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