in recent decades, evolutionary algorithms have been applied successfully in various water resource engineering and management issue especially in optimal operation of reservoirs. in this paper, a model based on shuffled complex evolution (sce) algorithm has been developed for modeling optimal operation of complex multi reservoir system of karkheh basin. the system includes 4 different agricult...

In this paper, a new approach for fitness evaluation of Genetic Algorithm (GA) is discussed. The approach is based on position of the genes in a chromosome. A random initial population sometimes may generate recessive strings that may increase the number of iterations. Also, it is hard to decide time to arrive at a particular solution. Chromosome’s strength is dependent on the arrangement of it...

Many interesting varieties of genetic algorithms have been designed and implemented in the last fifteen years. One way to improve our understanding of genetic algorithms is to identify properties that are invariant across these seemingly different versions. This paper focuses on invariants among genetic algorithms that differ along two dimensions: (1) the way user-defined objective function is ...

With the increasing complexity of engineering problems, the traditional, single-objective and deterministic optimization method can not meet people’s requirements. A multi-objective fuzzy optimization model of resource input is built for M chlor-alkali chemical eco-industrial park in this paper. First, the model is changed into the form that can be solved by genetic algorithm using fuzzy theory...

In this paper, we describe a novel approach to image quantisation by using a genetic algorithm to find near-optimal code words. Moving away from the traditional binary string representations of genetic data, the proposed system applies genetic operators directly on pixel blocks thus avoiding any conversion to a conventional format. To operate on the data this way, operators for fitness, combina...

Genetic Algorithms (GA's) are considered promising for music composition because they combine ‘creativity’ (ability to explore a large search space) with constraints (creative 'excess' is 'pruned' using a fitness function). A major difficulty with the use of GA's for this task is to define fitness functions which capture the aesthetic qualities of the wide range of successful melodies. In this ...

In this paper, we describe a novel approach to image quantisation by using a genetic algorithm to find near-optimal code words. Moving away from the traditional binary string representations of genetic data, the proposed system applies genetic operators directly on pixel blocks thus avoiding any conversion to a conventional format. To operate on the data this way, operators for fitness, combina...

In this paper we introduce a new generic selection method for Genetic Algorithms. The main difference of this selection principle in contrast to conventional selection models is given by the fact that it considers not only the fitness of an individual compared to the fitness of the total population in order to determine the possibility of being selected. Additionally, in a second selection step...

‎The most challenging task in dealing with Bayesian networks is learning their structure‎. ‎Two classical approaches are often used for learning Bayesian network structure;‎ ‎Constraint-Based method and Score-and-Search-Based one‎. ‎But neither the first nor the second one are completely satisfactory‎. ‎Therefore the heuristic search such as Genetic Alg...

A simple model of multi-agent three-dimensional construction is presented. The properties of this model are investigated. Based on these properties, a fitness function is defined to characterize the structured patterns that can be generated by the model. The fitness function assigns a value to each pattern. The choice of the fitness function is validated by the fact that human observers tend to...