This thesis focuses on the classic problem of investment per offspring. It is an attempt to (i) reconcile theoretical research with empirical methods that can be used to test theory, (ii) test a fundamental prediction that arises from classic theory, and (iii) test one of the more recent theoretical developments. We use Atlantic salmon (Salmo salar) as a model organism. Drawing from the classic Smith–Fretwell model, we provide defensible definitions of offspring fitness that can be used in empirical studies, and we show using simulation that the Weibull-1 statistical model provides the best estimates of optimal investment patterns. Next, we apply these methods to mark-recapture data collected for juvenile Atlantic salmon. This experiment supports the prediction that parental reproductive success is maximized by increasing investment per offspring when environmental conditions become unfavourable. Having verified this prediction, we test a general extension of classic theory which broadly suggests that large-bodied females decrease the quality of the offspring environment, such that larger females in a population ought to invest relatively heavily in investment per offspring. This might occur, for example, when larger females have a greater fecundity and if optimal investment per offspring increases with sibling competition among non-dispersive offspring. The results of this experiment generally do not support the idea that large females decrease the quality of the offspring environment in Atlantic salmon. Finally, we also provide evidence against a verbal hypothesis that attempts to explain inter-population variation in egg size of salmonids as an adaptation to population-specific spawning substrates. We conclude that the classic model of egg-size optimization can be a useful tool for understanding patterns of reproductive allocation in nature, but that investment per offspring is an extremely complex trait that cannot be fully understood by invoking a simple optimality model. Variation in investment per offspring, especially that which occurs within populations, is most parsimoniously attributed to the physiological factors (e.g., variation in testosterone levels), morphological constraints (e.g., the size of the pelvic aperture) and genetic factors (e.g., genetic correlations arising from pleiotropic genes) that affect this phenotype and that constrain adaptive evolution of this trait.

In this paper we study a complex real world workforce scheduling problem. We apply constructive search and variable neighbourhood search (VNS) metaheuristics and enhance these methods by using a variable fitness function. The variable fitness function (VFF) uses an evolutionary approach to evolve weights for each of the (multiple) objectives. The variable fitness function can potentially enhance any search based optimisation heuristic where multiple objectives can be defined through evolutionary changes in the search direction. We show that the VFF significantly improves performance of constructive and VNS approaches on training problems, and "learn" problem features which enhance the performance on unseen test problem instances.

Evolutionary algorithms are optimization techniques inspired by the actual evolution of biological species. They use conceptually simple process of two repeating phases of reproduction and fitness-based selection, that iteratively evolves each time better solutions. Evolutionary algorithms receive a lot of attention for being able to solve very hard optimization problems, where other optimization techniques might fail due to existence of many local optima. Wide range of different variants of evolutionary algorithms have been proposed. In this thesis, we will focus on the area of Estimation of Distribution Algorithms (EDA). When creating the next generation, EDAs transform the selected high-fitness population into a probability distribution. New generation is obtained by sampling the estimated distribution. We will design and and implement combinations of existing EDAs that will operate in business-specific environment, that can be characterized as tree-like structure of both discrete and continuous variables. Also, additional linear inequality constraints are specified to applicable solutions. Implemented application communicates with provided interfaces, retrieving the problem model specification and storing populations into database. Database is used to assign externally computed fitness values from...

The management of Water Distribution Systems (WDSs) involves making decisions about various operations in the network, including the scheduling of pump operations and setting of disinfectant dosing rates. There are often conflicting objectives in making these operational decisions, such as minimising costs while maximising the quality of the water supplied. Hence, the operation of WDSs can be very difficult, and there is generally considerable scope to improve the operational efficiency of these systems by improving the associated decision making process. In order to achieve this goal, optimisation methods known as Genetic Algorithms (GAs) have been successfully adopted to assist in determining the best possible solutions to WDS optimisation problems for a number of years. Even though there has been extensive research demonstrating the potential of GAs for improving the design and operation of WDSs, the method has not been widely adopted in practice. There are a number of reasons that may contribute to this lack of uptake, including the following difficulties: (a) developing an appropriate fitness function that is a suitable description of the objective of the optimisation including all constraints, (b) making decisions that are required to select the most appropriate variant of the algorithm, (c) determining the most appropriate parameter settings for the algorithm, and (d) a reluctance of WDS operators to accept new methods and approaches. While these are all important considerations, the correct selection of GA parameter values is addressed in this thesis. Common parameters include population size, probability of crossover, and probability of mutation. Generally, the most suitable GA parameters must be found for each individual optimisation problem, and therefore it might be expected that the best parameter values would be related to the characteristics of the associated fitness function. The result from the work undertaken in this thesis is a complete GA calibration methodology, based on the characteristics of the optimisation problem. The only input required by the user is the time available before a solution is required, which is beneficial in the WDS operation optimisation application considered, as well as many others where computationally demanding model simulations are required. Two methodologies are proposed and evaluated in this thesis, one that considers the selection pressure based on the characteristics of the fitness function, and another that is derived from the time to convergence based on genetic drift, and therefore does not require any information about the fitness function characteristics. The proposed methodologies have been compared against other GA calibration methodologies that have been proposed, as well as typical parameter values to determine the most suitable method to determine the GA parameter values. A suite of test functions has been used for the comparison, including 20 complex mathematical optimisation problems with different characteristics, as well as realistic WDS applications. Two WDS applications have been considered: one that has previously been optimised in the literature, the Cherry Hills-Brushy Plains network; and a real case study located in Sydney, Australia. The optimisation problem for the latter case study is to minimise the pumping costs involved in operating the WDS, subject to constraints on the system, including minimum disinfectant concentrations. Of the GA calibration methods compared, the proposed calibration methodology that considered selection pressure determined the best solution to the problem, producing a 30% reduction in the electricity costs for the water utility operating the WDS. The comparison of the different calibration approaches demonstrates three main results: 1. that the proposed methodology produced the best results out of the different GA calibration methods compared; 2. that the proposed methodology can be applied in practice; and 3. that a correctly calibrated GA is very beneficial when solutions are required in a limited timeframe. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1325448 / Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2008

For many real-world applications of evolutionary computation, the fitness function is obscured by random noise. This interferes with the evaluation and selection processes and adversely affects the performance of the algorithm. Noise can be effectively eliminated by averaging a large number of fitness samples for each candidate, but the number of samples used per candidate (the resampling rate) required to achieve this is usually prohibitively large and time-consuming. Hence there is a practical need for algorithms that handle noise without eliminating it. Moreover, the amount of noise (noise strength and distribution) may vary throughout the search space, further complicating matters. We study noisy problems for which the noise strength varies throughout the search space. Such problems have generally been ignored by previous work, which has instead generally focussed on the specific case where the noise strength is the same at all points in the search domain. However, this need not be the case, and indeed this assumption is false for many applications. For example, in games of chance such as Poker, some strategies may be more conservative than others and therefore less affected by the inherent noise of the game. This thesis makes three significant contributions in the field of noisy fitness functions: We present the concept of dynamic resampling. Dynamic resampling is a technique that varies the resampling rate based on the noise strength and fitness for each candidate individually. This technique is designed to exploit the variation in noise strength and fitness to yield a more efficient algorithm. We present several dynamic resampling algorithms and give results that show that dynamic resampling can perform significantly better than the standard resampling technique that is usually used by the optimisation community, and that dynamic resampling algorithms that vary their resampling rates based on both noise strength and fitness can perform better than algorithms that vary their resampling rate based on only one of the above. We study a specific class of noisy fitness functions for which we counterintuitively find that it is better to use a higher resampling rate in regions of lower noise strength, and vice versa. We investigate how the evolutionary search operates on such problems, explain why this is the case, and present a hypothesis (with supporting evidence) for classifying such problems. We present an adaptive engine that automatically tunes the noise compensation parameters of the search during the run, thereby eliminating the need for the user to choose these parameters ahead of time. This means that our techniques can be readily applied to real-world problems without requiring the user to have specialised domain knowledge of the problem that they wish to solve. These three major contributions present a significant addition to the body of knowledge for noisy fitness functions. Indeed, this thesis is the first work specifically to examine the implications of noise strength that varies throughout the search domain for a variety of noise landscapes, and thus starts to fill a large void in the literature on noisy fitness functions.

In introduction of work, I presented the state of the art of measuring insertion loss of the EMI filter and the description of individual methods of measuring is also included. After that I dealt with global optimization methods and some of them I theoretically described. Mainly PSO method was studied carefully, and the principle of optimization in details are presented in the thesis, because this method was used for optimization of the filters parameters in the thesis. Next part of work deals with theoretical and mathematical expression of insertion loss of the EMI filters. Due to the mathematical expression of the insertion loss, the values of parasitic elements of given filter have been optimized in different measuring systems. This optimization was applied on synthetic data. By this step the functionality of optimization was proved and also the fitness function has been tuned and optimised. The last part of thesis works with optimization on measured data of given filters. I found out inaccuracy in mathematical expression of given model of EMI filter, therefore it was necessary to come up with the new filter model. The accuracy increases by using the new filter model. In next step the new model was enlarged by new spurious components.

Master thesis is focused on optimization of cogeneration system for purpose of rating optimization methods and evaluating properties of these methods. For each method there is description together with block schemes. First part of thesis is devoted to description of methods and their comparison. Second part consists of development of hybrid algorithm, which is used to optimize cogeneration systém model. Each algorithm compared is together with hybrid algorithms included in annexes.

Na última década diversos algoritmos baseados em busca foram desenvolvidos para a geração de níveis em diferentes tipos de jogos. O espaço de busca para geração de níveis geralmente possui restrições, uma vez que a mecânica de um jogo define regras de factibilidade para os níveis. Em alguns métodos, a avaliação de factibilidade requer uma simulação com um agente inteligente que controla o jogo. Esse processo de avaliação geralmente possui ruído, causado por componentes aleatórios no simulador ou na estratégia do agente. Diversos trabalhos têm utilizado simulação como forma de avaliação de conteúdo, no entanto, nenhum deles discutiu profundamente a presença de ruído neste tipo de abordagem. Assim, esse trabalho apresenta um algoritmo genético capaz de gerar níveis factíveis que são avaliados por um agente inteligente em uma simulação ruidosa. O algoritmo foi aplicado a jogos de quebra-cabeças baseados em física com a mecânica do Angry Birds. Uma representação dos níveis em forma de indivíduos é introduzida, a qual permite que o algoritmo genético os evolua com características diferenciadas. O ruído na função de aptidão é tratado por uma nova abordagem, baseada em uma sistema de cache, que auxilia o algoritmo genético a encontrar boas soluções candidatas. Três conjuntos de experimentos foram realizados para avaliar o algoritmo. O primeiro compara o método de cache proposto com outros métodos de redução de ruído da literatura. O segundo mede a expressividade do algoritmo genético considerando as características estruturais dos níveis gerados. O último avalia os níveis gerados considerando aspectos de design (como dificuldade, imersão e diversão), os quais são medidos por meio de questionários respondidos por jogadores humanos via Internet. Os resultados mostraram que o algoritmo genético foi capaz de gerar níveis distintos que são tão imersíveis quanto níveis produzidos manualmente. Além disso, a abordagem de cache lidou apropriadamente com o ruído nos cálculos de aptidão, permitindo uma correta evolução elitista. / In the last decade several search-based algorithms have been developed for generating levels in different types of games. The search space for level generation is typically constrained once the game mechanics define feasibility rules for the levels. In some methods, evaluating level feasibility requires a simulation with an intelligent agent which plays the game. This evaluation process usually has noise, caused by random components in the simulator or in the agent strategy. Several works have used a simulation for content evaluation, however, none of them have deeply discussed the presence of noise in this kind of approach. Thus, this paper presents a genetic algorithm capable of generating feasible levels that are evaluated by an intelligent agent in a noisy simulation. The algorithm was applied to physics-based puzzle games with the Angry Birds mechanics. A level representation in the form of individuals is introduced, which allows the genetic algorithm to evolve them with distinct characteristics. The fitness function noise is handled by a new approach, based on a cache system, which helps the genetic algorithm finding good candidate solutions. Three sets of experiments were conducted to evaluate the algorithm. The first one compares the proposed cache approach with other noise reduction methods of the literature. The second one measures the expressivity of the genetic algorithm considering the structural characteristics of the levels. The last one evaluates design aspects (such as difficulty, immersion and fun) of the generated levels using questionnaires answered by human players via Internet. Results showed the genetic algorithm was capable of generating distinct levels that are as immersive as levels manually designed. Moreover, the cache approach handled properly the noise in the fitness calculations, allowing a correct elitist evolution.

Na última década diversos algoritmos baseados em busca foram desenvolvidos para a geração de níveis em diferentes tipos de jogos. O espaço de busca para geração de níveis geralmente possui restrições, uma vez que a mecânica de um jogo define regras de factibilidade para os níveis. Em alguns métodos, a avaliação de factibilidade requer uma simulação com um agente inteligente que controla o jogo. Esse processo de avaliação geralmente possui ruído, causado por componentes aleatórios no simulador ou na estratégia do agente. Diversos trabalhos têm utilizado simulação como forma de avaliação de conteúdo, no entanto, nenhum deles discutiu profundamente a presença de ruído neste tipo de abordagem. Assim, esse trabalho apresenta um algoritmo genético capaz de gerar níveis factíveis que são avaliados por um agente inteligente em uma simulação ruidosa. O algoritmo foi aplicado a jogos de quebra-cabeças baseados em física com a mecânica do Angry Birds. Uma representação dos níveis em forma de indivíduos é introduzida, a qual permite que o algoritmo genético os evolua com características diferenciadas. O ruído na função de aptidão é tratado por uma nova abordagem, baseada em uma sistema de cache, que auxilia o algoritmo genético a encontrar boas soluções candidatas. Três conjuntos de experimentos foram realizados para avaliar o algoritmo. O primeiro compara o método de cache proposto com outros métodos de redução de ruído da literatura. O segundo mede a expressividade do algoritmo genético considerando as características estruturais dos níveis gerados. O último avalia os níveis gerados considerando aspectos de design (como dificuldade, imersão e diversão), os quais são medidos por meio de questionários respondidos por jogadores humanos via Internet. Os resultados mostraram que o algoritmo genético foi capaz de gerar níveis distintos que são tão imersíveis quanto níveis produzidos manualmente. Além disso, a abordagem de cache lidou apropriadamente com o ruído nos cálculos de aptidão, permitindo uma correta evolução elitista. / In the last decade several search-based algorithms have been developed for generating levels in different types of games. The search space for level generation is typically constrained once the game mechanics define feasibility rules for the levels. In some methods, evaluating level feasibility requires a simulation with an intelligent agent which plays the game. This evaluation process usually has noise, caused by random components in the simulator or in the agent strategy. Several works have used a simulation for content evaluation, however, none of them have deeply discussed the presence of noise in this kind of approach. Thus, this paper presents a genetic algorithm capable of generating feasible levels that are evaluated by an intelligent agent in a noisy simulation. The algorithm was applied to physics-based puzzle games with the Angry Birds mechanics. A level representation in the form of individuals is introduced, which allows the genetic algorithm to evolve them with distinct characteristics. The fitness function noise is handled by a new approach, based on a cache system, which helps the genetic algorithm finding good candidate solutions. Three sets of experiments were conducted to evaluate the algorithm. The first one compares the proposed cache approach with other noise reduction methods of the literature. The second one measures the expressivity of the genetic algorithm considering the structural characteristics of the levels. The last one evaluates design aspects (such as difficulty, immersion and fun) of the generated levels using questionnaires answered by human players via Internet. Results showed the genetic algorithm was capable of generating distinct levels that are as immersive as levels manually designed. Moreover, the cache approach handled properly the noise in the fitness calculations, allowing a correct elitist evolution.

近幾年來，非線性時間數列分析一直是時間數列及計量經濟學者所熱衷的研究主題之一，而非線性時間數列結構改變的研究也越來越受到重視。其中的門檻自迴歸模式，雖具有線性模式所不能配適的特性，但模式建構的問題，一直是其在發展應用上的瓶頸。本研究擬以門檻自迴歸模式建構的流程並結合遺傳演算法的最佳化搜尋技術，架構出時間數列遺傳演算法，藉此演算法則及程序，全域性地搜尋最佳的門檻自迴歸模式。 / Non-linear time series analysis is a research topic which the schalors of time series and econometrics are intent on, and the research of structure change of non-linear time series is attentive. Threshold autoregressive model (TAR model) of non-linear time series has some characters which linear model fail to fit while the problem of how to find an appropriate threshold value is still attracted many researchers attention. In this paper, we present about searching the parameters for a TAR model by genetic algorithms.