As a result of ¡§the X-ray Phase Problem¡¨, traditional direct methods can¡¦t solve the structures of the large molecules. For exploring alternate methods, Wu-Pei Su applied simulated annealing to solve the structure of the large molecules and obtained success. Adopting his concept, we wrote a program for solving the structure of the molecules by C Program Language. And for decreasing the running time of the program, we introduced the concept of genetic algorithm into simulated annealing method.

Natural gas plants can have multiple owners for raw natural gas streams and processing facilities as well as for multiple products. Therefore, a proper cost allocation method is necessary for taxation of the profits from natural gas and crude oil as well as for cost sharing among gas producers. However, cost allocation methods most often used in accounting, such as the sales value method and the physical units method, may produce unacceptable or even illogical results when applied to natural gas processes. Wright and Hall (1998) proposed a new approach called the design benefit method (DBM), based upon engineering principles, and Wright et al. (2001) illustrated the potential of the DBM for reliable cost allocation for natural gas processes by applying it to a natural gas process. In the present research, a rigorous modeling technique for the DBM has been developed based upon a Taylor series approximation. Also, we have investigated a cost allocation framework that determines the virtual flows, models the equipment, and evaluates cost allocation for applying the design benefit method to other scenarios, particularly those found in the petroleum and gas industries. By implementing these individual procedures on a computer, the proposed framework easily can be developed as a software package, and its application can be extended to large-scale processes. To implement the proposed cost allocation framework, we have investigated an optimization methodology specifically geared toward economic optimization problems encountered in natural gas plants. Optimization framework can provide co-producers who share raw natural gas streams and processing plants not only with optimal operating conditions but also with valuable information that can help evaluate their contracts. This information can be a reasonable source for deciding new contracts for co-producers. For the optimization framework, we have developed a genetic-quadratic search algorithm (GQSA) consisting of a general genetic algorithm and a quadratic search that is a suitable technique for solving optimization problems including process flowsheet optimization. The GQSA inherits the advantages of both genetic algorithms and quadratic search techniques, and it can find the global optimum with high probability for discontinuous as well as non-convex optimization problems much faster than general genetic algorithms.

For many years research in artificial intelligence followed a symbolic paradigm which required a level of knowledge described in terms of rules. More recently subsymbolic approaches have been adopted as a suitable means for studying many problems. There are many search mechanisms which can be used to manipulate subsymbolic components, and in recent years general search methods based on models of natural evolution have become increasingly popular. This thesis examines a hybrid symbolic/subsymbolic approach and the application of evolutionary algorithms to a problem from each of the fields of shape representation (finding an iterated function system for an arbitrary shape), natural language dialogue (tuning parameters so that a particular behaviour can be achieved) and speech recognition (selecting the penalties used by a dynamic programming algorithm in creating a word lattice). These problems were selected on the basis that each should have a fundamentally different interactions at the subsymbolic level. Results demonstrate that for the experiments conducted the evolutionary algorithms performed well in most cases. However, the type of subsymbolic interaction that may occur influences the relative performance of evolutionary algorithms which emphasise either top-down (evolutionary programming - EP) or bottom-up (genetic algorithm - GA) means of solution discovery. For the shape representation problem EP is seen to perform significantly better than a GA, and reasons for this disparity are discussed. Furthermore, EP appears to offer a powerful means of finding solutions to this problem, and so the background and details of the problem are discussed at length. Some novel constraints on the problem's search space are also presented which could be used in related work. For the dialogue and speech recognition problems a GA and EP produce good results with EP performing slightly better. Results achieved with EP have been used to improve the performance of a speech recognition system.

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Facility Location problem refers to placing facilities (mostly vehicles) in appropriate locations to yield the best coverage with respect to other important factors which are specific to the problem. For instance in a fire station some of the important factors are traffic time, distribution of stations, time of the service and so on. Furthermore, budget limitation, time constraints and the great importance of the operation, make the optimum allocation very complex. In the past few years, several research in this area have been done to help managers by designing some effective algorithm to allocating facilities in the best way possible. Most early proposed models were focused on static and deterministic methods. In static models, once a facility assigns to a location, it will not relocate anymore. Although these methods could be utilized in some simple settings, there are so many factors in real world that make a static model of limited application. The demands may change over time or facilities may be dropped or added. In these cases a more flexible model is desirable, thus dynamic models are proposed to be used in such cases. Facilities can be located and relocated based on the situations. More recently, dynamic models became more popular but there were still many aspects of facility allocation problems which were challenging and would require more complex solutions. The importance of facility location problem becomes significantly more relevant when it relates to hospitals and emergency responders. Even one second of improvement in response time is important in this area. For this reason, we selected ambulance facility allocation problem as a case study to analyze this problem domain. Much research has been done on ambulances allocation. We will review some of these models and their advantages and disadvantages. One of the best model in this areas introduced by Rajagopalan. In this work, his model is analyzed and its major drawback is addressed by applying some modifications to its methodology. Genetic Algorithm is utilized in this study as a heuristic method to solve the allocation model.

Yes / The aim of this article is to apply and improve the species-conserving genetic algorithm (SCGA) to search multiple solutions of truss topology optimization problems in a single run. A species is defined as a group of individuals with similar characteristics and is dominated by its species seed. The solutions of an optimization problem will be selected from the found species. To improve the accuracy of solutions, a species mutation technique is introduced to improve the fitness of the found species seeds and the combination of a neighbour mutation and a uniform mutation is applied to balance exploitation and exploration. A real vector is used to represent the corresponding cross-sectional areas and a member is thought to be existent if its area is bigger than a critical area. A finite element analysis model was developed to deal with more practical considerations in modelling, such as the existence of members, kinematic stability analysis, and computation of stresses and displacements. Cross-sectional areas and node connections are decision variables and optimized simultaneously to minimize the total weight of trusses. Numerical results demonstrate that some truss topology optimization examples have many global and local solutions, different topologies can be found using the proposed algorithm on a single run and some trusses have smaller weights than the solutions in the literature.

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Title: Multiple sequence alignment using genetic algorithms Author: Zdeněk Pátek Department: Department of Software and Computer Science Education Supervisor: RNDr. František Mráz, CSc. Abstract: The thesis adresses the problem of multiple sequence alignment (MSA). It contains the specication of the proposed method MSAMS that allows to find motifs in biological sequences, to split sequences to blocks using the motifs, to solve MSA on the blocks and nally to assemble the global alignment from the aligned blocks and motifs. Motif search and MSA are both solved using genetic algorithms. The thesis describes the implementation of the method, conguration of its settings, benchmarking on the BAliBASE database and comparison to the ClustalW program. Experimental results showed that MSAMS can discover better alignments than ClustalW. Keywords: multiple sequence alignment, motif nding, genetic algorithms, ClustalW