Yes / Identification of fuzzy rules is an important issue in designing of a fuzzy neural network (FNN). However, there is no systematic design procedure at present. In this paper we present a genetic algorithm (GA) based learning algorithm to make use of the known membership function to identify the fuzzy rules form a large set of all possible rules. The proposed learning algorithm initially considers all possible rules then uses the training data and the fitness function to perform ruleselection. The proposed GA based learning algorithm has been tested with two different sets of training data. The results obtained from the experiments are promising and demonstrate that the proposed GA based learning algorithm can provide a reliable mechanism for fuzzy rule selection.

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Genetic algorithm and artificial life techniques are applied to the development of challenging and interesting opponents in a combat-based computer game. Computer simulations are carried out against an idealized human player to gather data on the effectiveness of the computer generated opponents.

This dissertation presents a study of linearization techniques that have been applied to power amplifiers in the cellular communication industry. The objective of this work is to understand the limitations of power amplifiers, specifically the limitations introduced by the use of spectrally efficient modulation schemes. The digitization of communication systems has favored the use of new techniques and technologies capable of increasing the efficiency of costly power amplifiers. The work explores traditional and digital linearization systems; an algorithm based on the principles of natural recombination is proposed to directly address the limitations of previous embodiments. Previous techniques, although effective, have significant implementation costs that increase exponentially with the increasing signal bandwidths. The proposed software-hardware architecture significantly reduces implementation costs and the overall complexity of the design without sacrificing performance. To fulfill the requirements of this study, multiple systems are implemented through simulation and closed-loop hardware. Both simulation and hardware embodiments meet the expected performance metrics, providing validation of the proposed algorithm. The application of the algorithm to memory power amplifier linearization is a new approach to adaptive digital pre-distortion using narrowband feedback. The work will show performance improvements on an amplifier with memory effects suggesting that this technique can be employed as a lower-cost solution to meet requirements when compared to typical system implementations.

The objective of thermal unit commitment is to schedule the on or off status and the real power outputs of units and minimize the system production cost during the period while simultaneously satisfying operational constraints. In this thesis, the Real Genetic Algorithms (RGA) and the Hybrid Taguchi-Genetic Algorithm (HTGA) approaches are presented to solve the thermal unit commitment problem, and comparison with the results obtained using GA. Then this thesis applied the systematic reasoning ability of the Taguchi method operated after mutation can promote the RGA efficiency. The objective of Taguchi method is to improve the quality of offsprings by optimizing themselves to generate a better result, because the offsprings produced randomly by crossover and mutation process is not necessary better than the parents. This method can not only enhance the neighborhood search, but can also search the optimum solution quickly to advance convergence. Finally, it will be shown that the HTGA outperforms RGA by comparing simulation results of unit commitment.

In the area of artificial intelligence, the development of Evolutionary Algorithms (EAs) has been very active, especially in the last decade. These algorithms started to evolve when scientists from various regions of the world applied the principles of evolution to algorithmic search and problem solving. EAs have been utilised successfully in diverse complex application areas. Their success in tackling hard problems has been the engine of the field of Evolutionary Computation (EC). Nowadays, EAs are considered to be the best solution to use when facing a hard search or optimisation problem. Various improvements are continually being made with the design of new operators, hybrid models, among others. A very important example of such improvements is the use of parallel models of GAs (PGAs). PGAs have received widespread attention from various researchers as they have proved to be more effective than panmictic GAs, especially in terms of efficacy and speedup. This thesis focuses on, and investigates, cellular Genetic Algorithms (cGAs)-a competitive variant of parallel GAs. In a cGA, the tentative solutions evolve in overlapped neighbourhoods, allowing smooth diffusion of the solutions. The benefits derived from using cGAs come not only from flexibility gains and their fitness to the objective target in combination with a robust behaviour but also from their high performance and amenability to implementation using advanced custom silicon chip technologies. Nowadays, cGAs are considered as adaptable concepts for solving problems, especially complex optimisation problems. Due to their structural characteristics, cGAs are able to promote an adequate exploration/exploitation trade-off and thus maintain genetic diversity. Moreover, cGAs are characterised as being massively parallel and easy to implement. The structural characteristics inherited in a cGA provide an active area for investigation. Because of the vital role grid structure plays in determining the effectiveness of the algorithm, cellular dimensionality is the main issue to be investigated here. The implementation of cGAs is commonly carried out on a one- or two-dimensional structure. Studies that investigate higher cellular dimensions are lacking. Accordingly, this research focuses on cGAs that are implemented on a three-dimensional structure. Having a structure with three dimensions, specifically a cubic structure, facilitates faster spreading of solutions due to the shorter radius and denser neighbourhood that result from the vertical expansion of cells. In this thesis, a comparative study of cellular dimensionality is conducted. Simulation results demonstrate higher performance achieved by 3D-cGAs over their 2D-cGAs counterparts. The direct implementation of 3D-cGAs on the new advanced 3D-IC technology will provide added benefits such as higher performance combined with a reduction in interconnection delays, routing length, and power consumption. The maintenance of system reliability and availability is a major concern that must be addressed. A system is likely to fail due to either hard or soft errors. Therefore, detecting a fault before it deteriorates system performance is a crucial issue. Single Event Upsets (SEUs), or soft errors, do not cause permanent damage to system functionality, and can be handled using fault-tolerant techniques. Existing fault-tolerant techniques include hardware or software fault tolerance, or a combination of both. In this thesis, fault-tolerant techniques that mitigate SEUs at the algorithmic level are explored and the inherent abilities of cGAs to deal with these errors are investigated. A fault-tolerant technique and several mitigation techniques are also proposed, and faulty critical data are evaluated critical fault scenarios (stuck at ‘1’ and stuck at ‘0’ faults) are taken into consideration. Chief among several test and real world problems is the problem of determining the attitude of a vehicle using a Global Positioning System (GPS), which is an example of hard real-time application. Results illustrate the ability of cGAs to maintain their functionality and give an adequate performance even with the existence of up to 40% errors in fitness score cells. The final aspect investigated in this thesis is the dynamic characteristic of cGAs. cGAs, and EAs in general, are known to be stochastic search techniques. Hence, adaptive systems are required to continue to perform effectively in a changing environment, particularly when tackling real-world problems. The adaptation in cellular engines is mainly achieved through dynamic balancing between exploration and exploitation. This area has received considerable attention from researchers who focus on improving the algorithmic performance without incurring additional computational effort. The structural properties and the genetic operations provide ways to control selection pressure and, as a result, the exploration/exploitation trade-off. In this thesis, the genetic operations of cGAs, particularly the selection aspect and their influence on the search process, are investigated in order to dynamically control the exploration/exploitation trade-off. Two adaptive-dynamic techniques that use genetic diversity and convergence speeds to guide the search are proposed. Results obtained by evaluating the proposed approaches on a test bench of diverse-characteristic real-world and test problems showed improvement in dynamic cGAs performance over their static counterparts and other dynamic cGAs. For example, the proposed Diversity-Guided 3D-cGA outperformed all the other dynamic cGAs evaluated by obtaining a higher search success rate that reached to 55%.

The interaction between coding and learning rules in neural nets (NNs), and between coding and genetic operators in genetic algorithms (GAs) is discussed. The underlying principle advocated is that similar things in "the world" should have similar codes. Similarity metrics are suggested for the coding of images and numerical quantities in neural nets, and for the coding of neural network structures in genetic algorithms. A principal component analysis of natural images yields receptive fields resembling horizontal and vertical edge and bar detectors. The orientation sensitivity of the "bar detector" components is found to match a psychophysical model, suggesting that the brain may make some use of principal components in its visual processing. Experiments are reported on the effects of different input and output codings on the accuracy of neural nets handling numeric data. It is found that simple analogue and interpolation codes are most successful. Experiments on the coding of image data demonstrate the sensitivity of final performance to the internal structure of the net. The interaction between the coding of the target problem and reproduction operators of mutation and recombination in GAs are discussed and illustrated. The possibilities for using GAs to adapt aspects of NNs are considered. The permutation problem, which affects attempts to use GAs both to train net weights and adapt net structures, is illustrated and methods to reduce it suggested. Empirical tests using a simulated net design problem to reduce evaluation times indicate that the permutation problem may not be as severe as has been thought, but suggest the utility of a sorting recombination operator, that matches hidden units according to the number of connections they have in common. A number of experiments using GAs to design network structures are reported, both to specify a net to be trained from random weights, and to prune a pre-trained net. Three different coding methods are tried, and various sorting recombination operators evaluated. The results indicate that appropriate sorting can be beneficial, but the effects are problem-dependent. It is shown that the GA tends to overfit the net to the particular set of test criteria, to the possible detriment of wider generalisation ability. A method of testing the ability of a GA to make progress in the presence of noise, by adding a penalty flag, is described.

Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market.

Data clustering involves partitioning data points into clusters where data points within the same cluster have high similarity, but are dissimilar to the data points in other clusters. The k-means algorithm is among the most extensively used clustering techniques. Genetic algorithms (GA) have been successfully used to evolve successive generations of cluster centers. The primary goal of this research was to develop improved GA-based methods for center selection in k-means by using heuristic methods to improve the overall fitness of the initial population of chromosomes along with crowding techniques to avoid premature convergence. Prior to this research, no rigorous systematic examination of the use of heuristics and crowding methods in this domain had been performed. The evaluation included computational experiments involving repeated runs of the genetic algorithm in which values that affect heuristics or crowding were systematically varied and the results analyzed. Genetic algorithm performance under the various configurations was analyzed based upon (1) the fitness of the partitions produced, and by (2) the overall time it took the GA to converge to good solutions. Two heuristic methods for initial center seeding were tested: Density and Separation. Two crowding techniques were evaluated on their ability to prevent premature convergence: Deterministic and Parent Favored Hybrid local tournament selection. Based on the experiment results, the Density method provides no significant advantage over random seeding either in discovering quality partitions or in more quickly evolving better partitions. The Separation method appears to result in an increased probability of the genetic algorithm finding slightly better partitions in slightly fewer generations, and to more quickly converge to quality partitions. Both local tournament selection techniques consistently allowed the genetic algorithm to find better quality partitions than roulette-wheel sampling. Deterministic selection consistently found better quality partitions in fewer generations than Parent Favored Hybrid. The combination of Separation center seeding and Deterministic selection performed better than any other combination, achieving the lowest mean best SSE value more than twice as often as any other combination. On all 28 benchmark problem instances, the combination identified solutions that were at least as good as any identified by extant methods.