Browsing by Subject "Genetic algorithms"
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Item A new approach of genetic-based EM algorithm for mixture models(2011-05) Abeysundara, Sachith P.; Seo, Byungtae; Trindade, A. AlexandreFinite mixture models have been receiving an important attention over the years in practical and theoretical point of view, but it is still challenging task to estimate a reasonable estimator based on the maximum likelihood method. The most widely used technique to solve the problem, to some extent, is the EM algorithm. Researchers have done a lot of work to improve the results of the EM algorithm by modifying its basic idea. This work present such an attempt to obtain better estimates for a finite normal mixture model. A traditional evolutionary technique, known as Genetic algorithm, is coupled with the EM algorithm to improve the estimates of the EM algorithm started with a random initial vector of parameters. The presented method is tested with the availability of a Non-penalized and Penalized likelihood functions. Based on results, we can see that the proposed method is always superior to the classical EM algorithm when one concerns the global maximizer in the mixture likelihood function.Item Application of genetic algorithms to the design of microstrip antennas, wire antennas and microwave absorbers(2003-05) Choo, Hosung; Ling, HaoItem Cultural enhancement of neuroevolution(2002) McQuesten, Paul Herbert; Miikkulainen, RistoAny transmission of behavior from one generation to the next via non–genetic means is a process of culture. Culture provides major advantages for survival in the biological world. This dissertation develops four methods that harness the mechanisms of culture to enhance the power of neuroevolution: culling overlarge litters, mate selection by complementary competence, phenotypic diversity maintenance, and teaching offspring to respond like an elder. The methods are efficient because they operate without requiring additional fitness evaluations, and because each method addresses a different aspect of neuroevolution, they also combine smoothly. The combined system balances diversity and selection pressure, and improves performance both in terms of learning speed and solution quality in sequential decision tasks.Item Evolutionary bilevel optimization for complex control problems and blackbox function optimization(2015-05) Liang, Jason Zhi; Miikkulainen, Risto; Stone, PeterMost optimization algorithms must undergo time consuming parameter tuning in order to solve complex, real-world control tasks. Parameter tuning is inherently a bilevel optimization problem: The lower level objective function is the performance of the control parameters discovered by an optimization algorithm and the upper level objective function is the performance of the algorithm given its parameterization. In the first part of this thesis, a new bilevel optimization method called MetaEvolutionary Algorithm (MEA) is developed to discover optimal parameters for neuroevolution to solve control problems. In two challenging benchmarks, double pole balancing and helicopter hovering, MEA discovers parameters that result in better performance than hand tuning and other automatic methods. In the second part, MEA tunes an adaptive genetic algorithm (AGA) that uses the state of the population every generation to adjust parameters on the fly. Promising experimental results are shown for standard blackbox benchmark functions. Thus, bilevel optimization in general and MEA in particular are promising approaches for solving difficult optimization tasks.Item General-purpose optimization through information maximization(2012-05) Lockett, Alan Justin; Miikkulainen, Risto; Ghosh, Joydeep; Mooney, Raymond; Ravikumar, Pradeep; Zitkovic, GordanThe primary goal of artificial intelligence research is to develop a machine capable of learning to solve disparate real-world tasks autonomously, without relying on specialized problem-specific inputs. This dissertation suggests that such machines are realistic: If No Free Lunch theorems were to apply to all real-world problems, then the world would be utterly unpredictable. In response, the dissertation proposes the information-maximization principle, which claims that the optimal optimization methods make the best use of the information available to them. This principle results in a new algorithm, evolutionary annealing, which is shown to perform well especially in challenging problems with irregular structure.Item Genetic algorithms with functional mutation and mating operators in time series data mining(Texas Tech University, 2004-08) Huang, JianyongRecently, genetic algorithms (GAs) and artificial neural networks (ANNs) have been widely used in time series data mining (TSDM). Both GAs and ANNs are inspired from natural processes. A GA can be used to find optimized parameters for a given model, while an ANN has the ability to approximate unknown functions to any degree of desired accuracy without knowing the model. There are some limitations of using GAs or ANNs individually in TSDM. For example, ANNs generally use backpropagation learning algorithms, which are based on the deepest descent algorithm. Therefore, a solution from the .A.NN usually is a local optimized solution. The purpose of this thesis work is to develop innovative algorithms which can overcome the limitation of using GAs or ANNs solely in TSDM. The first part of this research involves designing a new genetic algorithm (called mGA), which can analyze not only polynomial but also non-polynomial time series. The mGA automatically searches a polynomial function with minimal degree for a non-polynomial time series. The rest of this research focuses on developing a neural network based genetic algorithm (called nGANN). The nGANN represents a chromosome as a neural network and uses genetic operators to select a global solution for a lime series. The nGANN introduces a new mating scheme (called NN _ mate), which uses a backpropagation learning network to produce offsprings. Therefore, NN mate can mate two parents with different models. The solution found by the nGANN has two attractive features: a network with small number of hidden neurons and a small mean squared error. From the solution network, h is possible to discover some relationships among different variables. Three different types of lime series data are used to evaluate the performance of the above algorithms, the two algorithms work well for one-variable polynomial and one-variable non-polynomial time series data. For two or more variables, the above algorithms do not produce very good results. In the last part of this thesis, future work is discussed.Item Heuristics for the family scheduling problems to minimize total tardiness(Texas Tech University, 2002-12) Chantaravarapan, SamarnRecently, family scheduling problems have attracted several researchers because of its real-life applications. The family scheduling problems involve multiple job types where jobs are classified into specific families by their similarity characteristics. Setup occurs only when the two consecutive jobs are from different families. An example of application of family scheduling problems is the colored plastic injecfion machine. Changing from one color to another color requires a setup. The purpose of this research is to implement methodologies to minimize total tardiness on single machine problems. However, the complexity of the proposed problem is NP-Hard. Seeking for optimal solutions for large problem sizes within a reasonable time is quite questionable. Therefore, this research aims to implement good heuristics that could provide good solutions within a reasonable time. This research can be divided into two main parts: problems with sequence independent setups and problems with sequence dependent setups. Integer programming models for both problems are proposed in this research. In case of problems with sequence independent setups, three constructive heuristics and five improving heuristics are proposed. The constructive heuristics are implemented to provide initial sequences for improving heuristics. In case of problems with sequence dependent setups, a hybrid genetic algorithm (HGA) is presented. The implementation of the proposed HGA is discussed thoroughly in this study. Furthermore, the proper genetic algorithm settings, such as crossover probability and population size, can enhance the performance of the algorithm. Thus, prior to investigating the performance of the proposed HGA, a pilot study is performed to determine proper genetic algorithm values. Experiments for each problem can be divided into two parts: experiments with small problem sizes, and experiments with large problem sizes. In the case of experiments of small problem sizes, the solutions of heuristics/HGA were compared with the solutions from CPLEX solver. In the experiments of large problem sizes, the solutions of heuristics/HGA were compared among each other. Furthermore, nonparametric tests, such as the Friedman tests and Kruskal-Wallis tests, were performed to investigate the effect of parameters, such as number of jobs and number of families, on the performance of heuristics/HGA.Item Processor allocation, message scheduling, and algorithm selection for parallel space-time adaptive processing(Texas Tech University, 2000-08) West, Jack M.The minimization of execution time (which includes both computation and communication components) and/or the maximization of throughput are of great significance in embedded parallel environments. Given tight system constraints associated with applications in these environments, it is imperative to efficiently map the tasks and/or data of an application onto the processors so as to reduce the imposed inter-processor communication traffic. In addition to mapping the tasks and data to the processors in an efficient manner, it is also important to schedule the communication of messages during phases of data movement so as to minimize network contention in an attempt to attain the smallest possible communication time. In this instance, mapping and scheduling can be classified as optimization problems, where the performance of the parallel system is vastly impacted by the optimization of both mapping and scheduling. This dissertation involves optimizing the mapping of data and the scheduling of messages for a class of signal processing techniques known as space-time adaptive processing (STAP). An objective function is proposed to measure the quality of data mapping to processing elements of a parallel system for a STAP algorithm. The objective function is a cost metric that provides a quantitative measurement of the message traffic generated during phases of data movement based on the mapping of data to processors on a parallel system. The results show significant differences in the quality of data mappings using the proposed objective function. A genetic algorithm (GA) based approach for solving the message scheduling optimization problem is proposed, and numerical results fi"om different scenarios are provided. The GA-based optimization is performed off-line, and the results of this optimization are static schedules for each processing element in the parallel system. These static schedules are then implemented in the on-line parallel STAP application. The results of this research illustrate significant improvement in communication time performance is possible using the proposed GA-based approach to scheduling.Item Statistical inference on relative performance of genetic algorithms: first results on the significance of knowledge, direction, and representation in three small network structures(Texas Tech University, 1997-05) Langevin, Greg J.A central question in GA research is the consistency or robustness of GA operators given small changes in a search [problem] space. To determine consistency, a small network structure, which consists of ten nodes and twenty-three arcs, is created. A second network structure is created by adding six arcs to the original network structure. A third network structure adds a node and twelve arcs to the original network structure; the placement of the node causes the elimination of one of the original arcs. If a set of GA operators performs well in one network structure, but does not perform well in a similar structure, the implication that can be inferred is that GA operators have to be "reworked" for each and every problem space. On the other hand, if the performance of the GA is consistent over these network structures, the implication is that it may be possible to find an optimal set of GA operators for sets of similar structures or problem types. Most GA applications use optimization criteria as performance measures (ENCORE, 1997). The performance measure for the GA in this study is based on its ability to explore the search space. The GA is instructed to find all feasible paths through these network structures, with a feasible path being defined as a solution that does not violate any constraints, expressed in the algebraic form, of a Shortest Route Model (see Chapter III, Experimental Design). Should the GA find all feasible paths, the search space can be inferred to have been adequately explored. Should the GA consistently find all feasible paths, (in most or all of fifty runs), the GA is said to be effective. If the GA is shown to be effective, then the performance measure becomes a question of speed; in terms of the number of generations, how fast did the GA find all feasible solutions? Efficiency, in this case, is defined as an effective GA that finds feasible solutions in relatively few generations.