Building a rule-based classification system for a training data set is an important research topic in the area of data mining, knowledge discovery and expert systems. Recently, the GA-based fuzzy approach is shown to be an effective way to design an efficient evolutionary fuzzy system. In this thesis a three layers genetic algorithm with Simulated Annealing for selecting a small number of fuzzy if-then rules to building a compact fuzzy classification system will be proposed. The rule selection problem with three objectives: (1) maximize the number of correctly classified patterns, (2) minimize the number of fuzzy if-then rules, and (3) minimize the number of required features. Genetic algorithms are applied to solve this problem. A set of fuzzy if-then rules is coded into a binary string and treated as an in-dividual in genetic algorithms. The fitness of each individual is specified by three ob-jectives in the combinatorial optimization problem. Simulated annealing (SA) is op-tionally cooperated with three layers genetic algorithm to effectively select some layer control genes. The performance of the proposed method for training data and test data is ex-amined by computer simulations on the iris data set and spiral data set, and comparing the performance with the existing approaches. It is shown empirically that the pro-posed method outperforms the existing methods in the design of optimal fuzzy sys-tems.

When we use the FDTD method to analyze electromagnetic problems, it has to properly discretize the space and time. Automeshing can non-uniformly discretize the simulated structure and generate gradual grids. To improve the efficiency of automeshing, we optimize the parameter of automeshing using the genetic algorithm. Without sacrificing accuracy, it searches a suitable ratio to reduce the generated grids and to save simulation time. At last, we optimize the PIFA using genetic algorithm and search automatically the height of the substrate and the feed position in order to obtain optimal performance. When we use the genetic algorithm, it is the key point to define an objective function evaluating the fitness of the optimized problem. It is important that the function has to appropriately describe the performance at that time.

In airline industry scheduling problems, the aircraft routing and the aircrew pairing problems are highly related to fueling and personnel costs. When performing aircraft routing and aircrew pairing, several objectives, such as the ground-turn around time, flow balance, transition time, number of deadheads, number of layovers, flying time, and flight duty period should be considered. It is difficult to optimize these conflicting objectives simultaneously. Many issues are yet to be solved as follows. 1. Most researches related to the aircraft routing and aircrew pairing problems use set partitioning or set covering models. Planners must (1) enumerate several possible subsets of flights, (2) assign costs, and (3) check feasibilities simultaneously. This is time-consuming since the numbers of whole subsets are exponential values to the problem size. 2. The number of enumerated subsets is usually too small to cover the whole solution space. Therefore, even if the optimal solution is found, it is just a local optimal solution of the enumerated subsets. 3. When using traditional optimization algorithms to find a combination of these subsets with minimal cost, it should be ensured that all flights should be covered exactly once. This causes the overheads of checking the number of coverage. 4. In traditional solution methods, the number of required aircrafts and crewmembers cannot be pre-specified since these numbers can only be obtained when the optimization algorithm is completed. 5. All enumerated subsets should be assigned cost values according to various objectives, such as transition time, number of deadheads, number of layovers, flying time, and flight duty period. The cost values are difficult to assign since it is highly dependent on domain knowledge, and usually nonlinear. Also, inappropriate cost values will cause bias in optimization, and ambiguity among all factors due to single objective formulation. Hence, to overcome these problems, we propose several enhancements in both formulation and the solution stages. In the formulation stage, we propose a novel permutation-based model with multiple objectives, which has the following features. 1. The proposed permutation-based model can save the overheads of pre-enumerating possible sub-solutions 2. The permutation-based model can cover the whole solution space. Hence, it has more chance to find out the global optimal solution. 3. The proposed permutation-based model can ensure that each flight can be covered exactly once to save the overheads of checking the number of coverage. 4. The proposed permutation-based model can provide a new way to pre-specify the number of aircrafts or group number of crewmembers. 5. Taking the advantage of multiobjective formulation, various objectives are considered separatively instead of assigning cost values. All objective can be considered individually even if they have different definitions of optimality or scales. In the solution stage, we apply the MOI-based MGA (MMGA) to solve the problems of aircraft routing and crew pairing. MMGA is originally proposed to solve numerical controller design problem. By using MMGA, designers can configure the ranges of solutions via adjusting an auxiliary vector of performance indices. To make MMGA more suitable for solving the aircraft routing and aircrew pairing problems, some enhancements are added, such as chromosome encoding scheme, repairing strategy, crossover, and mutation operations. This approach has following features. 1. In both aircraft routing and aircrew pairing problems, the permutation-based encoding scheme, which is the same as the formulation model, can ensure all flights be covered once. 2. Moreover, in the crew pairing problem, the sectional permutation-based encoding scheme, which divides the flights into three sections, such as earlier flights, later flights, and floating flights, can enhance MMGA to find out optimal solutions which satisfy the flight duty period objective. 3. Also, to overcome the large violations caused by random generation of candidate solutions, we use a repairing strategy, which repairs all generated solutions by reordering the sequences of flights according to departure times. 4. The sectional order-based crossover can have a more stable evolution than the widely-used partial mapped crossover. Also, it can make the newborn offspring keep the features of three sections defined in the encoding scheme. 5. Also, the sectional mutation can inherit the advantages of the widely-used reciprocal mutation and keep the features of three sections defined in the encoding scheme. In the aircraft routing problem, experiments show that MMGA can find out optimal flight schedules under the condition of sufficient aircrafts. On the other aspect, when the number of aircrafts is insufficient, planners can modify the obtained solutions by a little retiming process when the number of violations is small. In the aircrew pairing problem, experiments indicate the proposed approach can solve the aircrew pairing problem with minimal group number of crewmembers which is verified by a branch-and-bound approach. By using MMGA, the problems of aircraft routing and aircrew pairing can be solved efficiently and effectively. In other words, planners can solve these problems in a short time period instead of enumeration and feasibility checking by traditional methods. Via the proposed approach, planners can further consider more important issues, such as to suggest better schedules with lower cost and higher benefit.

The microarray technology plays an important role of clinical oncology field. The patient can be diagnosed a symptom about cancer through microarray data. Currently, to solve classification of microarray data is still a wild open issue. Existing methods may have a good performance, but need to spend much time to analyze microarray data, such as SVM. In this thesis, we propose a novel GA-Fuzzy-based voting mechanism to find genes which affect the symptom to better diagnose patient. The proposed algorithm can blur the boundary between classes to handle the ambiguous regions. In order to simulate the gene selection mechanism, we proposed upper bound £\-Cut and lower bound £\-Cut in voting mechanism. Two groups of data collected from the literature are used to test the performance of the proposed algorithm. In the first group of dataset, experimental results show that the accuracies of five datasets using the proposed algorithm are better than those methods proposed by Pochet et al. But, there are the four datasets which the accuracies using the proposed algorithm are a little bit worse than the methods proposed by Pochet et al. For the second group of dataset, the accuracies of seven datasets using the proposed algorithm are better than KerNN proposed by Xiong and Chen. But, there are four datasets which the accuracies using the proposed algorithm are worse than KerNN proposed by Xiong and Chen. Nevertheless, experimental results show that the proposed algorithm performs the best for multi-class data.

Various approaches have been proposed for finding the longest common subsequence (LCS) of two sequences. The time complexities of these algorithms are usually $O(n^2)$ in the worst case, where $n$ is the length of input sequences. However, these algorithms would become infeasible when the input length, $n$, is very long. Recently, the $k$-LCS $(k ≥ 2)$ problem has become more attractive. Some algorithms have been proposed for solving the problem, but the execution time required for solving the $k$-LCS problem is still too long to be practical. In this thesis, we propose a genetic algorithm for solving the $k$-LCS problem with time complexity $O(Gpk(n + |P_j|))$, which $G$ is the number of generations, $p$ is the number of template patterns, $k$ is the number of input sequences, $n$ and $|P_j|$ are the length of input sequences and the length of template patterns, respectively. As our experimental results show, when $k$ is 20 and $n$ is 1000, the performance ratio ($|CS|/|LCS|$) of our algorithm is greater than 0.8, where $|CS|$ denotes the length of the solution we find, and $|LCS|$ represents the length of the real (optimal) LCS. Comparing the performance ratios with Expansion Algorithm and BNMAS Algorithm, our algorithm is much better than them when the number of input sequences varies from 2 to 20 and the length of the input sequences varies from 100 to 2000.

The domain of computer music is an interesting area which combines computer science and music art. We propose a music accompaniment system using a variable-length genetic algorithm. Via the system one can make the music corresponded to his demands. In the style music accompaniment we analyze some important characteristic of pop music, and propose a new chromosome representation scheme to include the concept of rhyme, chord and melody. Chord progression is used as one of the evaluation criterions in this thesis. The system allows a user to input melody, to select emotion and rhyme, and the system will automatically generate the appropriate accompaniment based on the database compiled from some music theory relating to the chord progression. In addition, the system allows a user to select his favorite accompaniment that generated by the system. Based on the user selected accompaniment the system will generate similar accompaniments for the user.

The multiobjective genetic algorithm can be used to optimize two conflicting objectives, oil production and polymer utility factor in polymer flood design. This approach provides a set of optimal solutions which can be considered as trade-off curve (Pareto front) to maximize oil production while preserving polymer performance. Then an optimal polymer flood design can be considered from post-optimization analysis. A 2D synthetic example, and a 3D field-scale application, accounting for geologic uncertainty, showed that beyond the optimal design, a relatively minor increase in oil production requires much more polymer injection and the polymer utility factor increases substantially.

Flexible job-shop scheduling problem (FJSP) is a generalization of the classical job-shop scheduling problem (JSP). It takes shape when alternative production routing is allowed in the classical job-shop. However, production scheduling becomes very complex as the number of jobs, operations, parts and machines increases. Until recently, scheduling problems were studied assuming that all of the problem parameters are known beforehand. However, such assumption does not reflect the reality as accidents and unforeseen incidents happen in real manufacturing systems. Thus, an optimal schedule that is produced based on deterministic measures may result in a degraded system performance when released to the job-shop. For this reason more emphasis is put towards producing schedules that can handle uncertainties caused by random disruptions. The current research work addresses solving the deterministic FJSP using evolutionary algorithm and then modifying that method so that robust and/or stable schedules for the FJSP with the presence of disruptions are obtained. Evolutionary computation is used to develop a hybridized genetic algorithm (hGA) specifically designed for the deterministic FJSP. Its performance is evaluated by comparison to performances of previous approaches with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. After that, the previously developed hGA is modified to find schedules that are quality robust and/or stable in face of random machine breakdowns. Consequently, a two-stage hGA is proposed to generate the predictive schedule. Furthermore, the effectiveness of the proposed method is compared against three other methods; two are taken from literature and the third is a combination of the former two methods. Subsequently, the hGA is modified to consider FJSP when processing times of some operations are represented by or subjected to small-to-medium uncertainty. The work compares two genetic approaches to obtain predictive schedule, an approach based on expected processing times and an approach based on sampling technique. To determine the performance of the predictive schedules obtained by both approaches with respect to two types of robustness, an experimental study and Analysis of Variance (ANOVA) are conducted on a number of benchmark problems.

Cochlear implant (CI) users have traditionally reported less enjoyment and have performed more poorly on tasks of music perception (timbre, melody and pitch) than their normal hearing (NH) counterparts. The enjoyment and perception of music can be affected by the MAP programmed into a user’s speech processor, the parameters of which can be altered to change the way that a CI recipient hears sound. However, finding the optimal MAP can prove challenging to clinicians because altering one parameter will affect others. Until recently the only way to find the optimal MAP has theoretically been to present each potential combination of parameters systematically, however this is impractical in a clinical setting due to the thousands of different potential combinations. Thus, in general, clinicians can find a good MAP, but not necessarily the best one. The goal of this study was to assess whether a Genetic Algorithm would assist clinicians to create a better MAP for music listening than current methods. Seven adult Nucleus Freedom CI users were assessed on tasks of timbre identification, melody identification and pitch-ranking using their original MAP. The participants then used the GA software to create an individualised MAP for music listening (referred to as their “GA MAP”). They then spent four weeks comparing their GA and original MAPs in their everyday life, and recording their listening experiences in a listening diary. At the end of this period participants were assessed on the same timbre, melody, and pitch tasks using their GA MAP. The results of the study showed that the GA process took an average of 35 minutes (range: 13-72 minutes) to create a MAP for music listening. As a group, participants reported the GA MAP to be slightly better than their original MAP for music listening, and preferred the GA MAP when at the cinema. Participants, on average, also performed significantly better on the melody identification task with their GA MAP; however they were significantly better on the half-octave interval pitch ranking task with their original MAP. The results also showed that participants were significantly more accurate on the single-instrument identification task than the ensemble instrument identification task regardless of which MAP they used. Overall, the results show that a GA can be used to successfully create a MAP for music listening, with two participants creating a MAP that they decided to keep at the conclusion of the study.

Active flow control is an area of heightened interest in the aerospace community. Previous research on flow control design processes heavily depended on trial and error and the designers knowledge and intuition. Such an approach cannot always meet the growing demands of higher design quality in less time. Successful application of computational fluid dynamics (CFD) to this kind of control problem critically depends on an efficient searching algorithm for design optimization. CFD in conjunction with Genetic Algorithms (GA) potentially offers an efficient and robust optimization method and is a promising solution for current flow control designs. Current research has combined different existing GA techniques and motivation from the two-jet GA-CFD system previously developed at the University of Kentucky propose the applications of a real coded Continuous Genetic Algorithm (CGA) to optimize a four-jet and a synthetic jet control system on a NACA0012 airfoil. The control system is an array of jets on a NACA0012 airfoil and the critical parameters considered for optimization are the angle, the amplitude, the location, and the frequency of the jets. The design parameters of a steady four-jet and an unsteady synthetic jet system are proposed and optimized. The proposed algorithm is built on top of CFD code (GHOST), guiding the movement of jets along the airfoils upper surface. The near optimum control values are determined within the control parameter range. The current study of different Genetic Algorithms on airfoil flow control has been demonstrated to be a successful optimization application.