Bridge maintenance activities are often budgeted, scheduled, and conducted for networks of bridges with different ages, types, and conditions, which can make bridge network maintenance management challenging. In this study, we propose an improved maintenance planning model based on GENETIC algorithm for a network of bridges to bring a long-term perspective to the lifespan of bridges. To test the applicability and efficiency of the model, it was applied to a network of 100 bridges in one of the south-western provinces of Iran. The results of the model implementation showed considerable potential for improvement over the currently adopted model for bridge maintenance planning.

Risk-return tradeoff and its analysis in alternative investments as a classic goal of finance have been the main subject of many researches in financial management. The use of technical indicators is a portfolio management tools. This research aims to use these indicators in mining stocks trading rules. The period of investigation is from beginning of 1388 until the end of 1393 and the sample of study is including 216 companies listed in TSE. In the period from 1388 to 1390 by using technical indicators and GENETIC algorithm with aim for maximize return and minimize risk, we obtain a model for portfolio optimization and in the period from 1391 to 1393 this model was used in portfolio management. In order to evaluate this model, the results were compared with the market index and found that by using technical indicators can outperform the market.

Applying elastic methods in the design of steel moment resisting frames (SMRF) and not recognizing the redistribution of moments in the inelastic range, do not guarantee a suitable seismic behavior in earthquakes. In order to be able to predict and control the inelastic behavior under seismic loading and to determine the corresponding load factor, the design of SMRF is studied in this paper. Classic concepts of plastic analysis and GENETIC ALGORITHMS are combined to arrive at an optimal proportioning of the frame members. Various examples, along with studies on the parameters of the employed GENETIC algorithm are also presented within this work.

In this paper, a mathematical programming model for set covering problems (SCPs) is presented and developed which is close to real-world cases. In this proposed model, m customers are covered by n facilities in such a way that the distance between facilities must not be less than the available defined limit Due to nonlinearity of the model, the traditional optimization methods cannot solve large-sized problems in a reasonable amount of computational time. Thus in this paper, a GENETIC algorithm is proposed as a well-known metaheuristic method to solve such a hard problem. To show the efficiency the proposed algorithm, 36 test problems classified into 9 groups are generated at random and then they are solved by GENETIC ALGORITHMS. The associated results are compared with the Lingo 6 software package. These results show the efficiency and validation of the proposed algorithm.

In this paper multi-objective GENETIC ALGORITHMS were employed for Pareto approach optimization of turboprop engines. The considered objective functions are used to maximize the specific thrust, propulsive efficiency, thermal efficiency, propeller efficiency and minimize the thrust specific fuel consumption. These objectives are usually conflicting with each other. The design variables consist of thermodynamic parameters (compressor pressure ratio, turbine temperature ratio, Mach number) and propeller geometric parameters (blade activity factor and integrated design lift coefficient). Main effect of the design variables are calculated to recognize which design variables has the effect on the objective functions. Group method of data handling (GMDH) type neural networks is used for modeling and prediction of propeller efficiency using aerodynamic variables obtained by some experimental data. Relationships among design variables and optimal objectives functions have been obtained by non-dominated sorting GENETIC algorithm, (NSGA-II) with a new diversity preserving mechanism in multi-objective optimization. The pareto solutions are obtained for both two and five objective optimization processes. For two-objective optimization, different pairs of objectives have been selected. More ever, these objectives have also considered for a five-objective optimization problem. Variables based on this pareto front, indicated the best design point of objective functions. These results also showed that Pareto solutions of five-objective optimization provide more choices for optimal design of turboprop engines.

The Capacitated Clustering Problem (CCP) is a classical location problem with various applications in data mining. In the capacitated clustering problem, a set of n entities is to be partitioned into p disjoint clusters, such that the total dissimilarity within each cluster is minimized subject to constraints on maximum cluster capacity. Dissimilarity of a cluster is the sum of the dissimilarities between each entity that belongs to the cluster and the median associated with the cluster. In this paper two solution methods proposed for the problem. First method is a simulation annealing algorithm which uses different neighborhood structures randomly. The second method is a GENETIC algorithm approach which strengthened by a heuristic local search method. Computational results of test samples from literature demonstrate the robustness and efficiency of the proposed solution methods. This confirms that the proposed algorithm provides high quality solutions in reasonable time.

One of the major solutions for sustainable use of resources is official transportation system. Nowadays, the current transportation systems are determined optionally by people opinions, whereas this choice is not optimum. Therefore, a method must be taken due to a model to solve this problem efficiently. On the other hand, if the number of employees is considerable in a company, the problem area will be increased and using the mathematic ALGORITHMS will be difficult. Therefore in this paper the authors tried to reduce the problem’s search area by simple clustering method and then searched optimum path for employees in each cluster by population-based GENETIC Algorithm.But one of problems about GENETIC Algorithm using operations are appropriate for problematic conditions. In this paper the authors tried to develop the problem- solving conditions by using the appropriate cross over and mutation operations and then decrease spend time for finding the optimum solution. This algorithm is used in a part of Tehran city, and the information refers to 2006. By using the developed algorithm, on one hand, problem is responsive and the on the other hand problem is converged to optimum answer with lower repetition number in comparison with GENETIC method with simple operations and it has high repeatable test. At the end, the authors propose some suggestions to close the problem’s condition to real world condition and using some other population-based ALGORITHMS.

The reports show a rapid growth in the numbers of attacks to the information and communication systems. Also, we witness smarter behaviors from the attackers. Thus, to prevent our systems from these attackers, we need to create smarter intrusion detection systems. In this paper, a new intelligent intrusion detection system has been proposed using GENETIC ALGORITHMS. In this system, at first, the network connection features were ranked according to their importance in detecting attack using information theory measures. Then, the network traffic linear classifiers based on GENETIC ALGORITHMS have been designed. These classifiers were trained and tested using KDD99 data sets. A detection engine based on these classifiers was build and experimented. The experimental results showed a detection rate up to 92.94%. This engine can be used in real-time mode.