Microarray analysis of gene expression from different cells and in different environmental conditions provides a means of identifying genes that share similar expression. Many computational approaches for discovering ...
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Microarray analysis of gene expression from different cells and in different environmental conditions provides a means of identifying genes that share similar expression. Many computational approaches for discovering transcription factor binding sites have been offered in the literature. However, the majority of these approaches are organism-specific, require exhaustive calculation, or do not report the results back to the user in a friendly manner such as through the use of a nucleotide likelihood matrix. We approached the problem of de novo TFBS identification by using evolutionary computation to search for common sequence motifs across multiple upstream sequences without pre-alignment (Fogel et al., 2004).
evolutionary algorithms are, fundamentally, stochastic search procedures. Each next population is a probabilistic function of the current population. Various controls are available to adjust the probability mass funct...
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evolutionary algorithms are, fundamentally, stochastic search procedures. Each next population is a probabilistic function of the current population. Various controls are available to adjust the probability mass function that is used to sample the space of candidate solutions at each generation. For example, the step size of a single-parent variation operator can be adjusted with a corresponding effect on the probability of finding improved solutions and the expected improvement that will be obtained. Examining these statistics as a function of the step size leads to a 'fitness distribution', a function that trades off the expected improvement at each iteration for the probability of that improvement, This pager analyzes the effects of adjusting the step size of Gaussian and Cauchy mutations, as well as a mutation that is a convolution of these two distributions. The results indicate that fitness distributions can be effective in identifying suitable parameter settings for these operators. Some comments on the utility of extending this protocol toward the general diagnosis of evolutionary algorithms is also offered. (C) 1999 Elsevier Science Ireland Ltd. All rights reserved.
Experimental studies are prevalent in evolutionary computation (EC), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scient...
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This study focuses on the design of a segmented thermoelectric generator system to maximize the system output power. Skutterudite, (Sr, Ba, Yb)(y)Co4Sb12 with 9.1% In0.4Co4Sb12, is used as the n-type element, while DD...
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This study focuses on the design of a segmented thermoelectric generator system to maximize the system output power. Skutterudite, (Sr, Ba, Yb)(y)Co4Sb12 with 9.1% In0.4Co4Sb12, is used as the n-type element, while DD0.59Fe2.7Co1.3Sb11.8Sn0.2 is used as the p-type one. They both are employed as the hot side segments. Alternatively, hydrothermal synthesized nanostructure thermoelectric material (Bi0.4Sb1.6Te3) is chosen as the cold side segments. To achieve the optimum design, a numerical method is developed, while the multi-objective genetic algorithm is adopted. The evolutionary computation processes during seeking the optimum combination of the segments are visualized, and it is found that four generations are enough for reaching the target. With the leg length of 3 mm, the optimum n-type and p-type cold side segment lengths are 0.5 mm and 0.78 mm, respectively. Compared to the equal-segmented thermoelectric couple, the optimized couple at a temperature difference of 400 K can increase the output power by 21.94% and its efficiency is 14.05% which is much higher than conventional thermoelectric generators. The theory of impedance matching does not apply to the segmented thermoelectric generator. The heat flux distribution in the couple is dependent on the temperature difference. Overall, the segmented elements with evolutionary computation design is a promising tool for intensifying thermoelectric generator performance.
Seven-day-ahead forecasting models of Cylindrospermopsis raciborskii in three warm-monomictic and mesotrophic reservoirs in south-east Queensland have been developed by means of water quality data from 1999 to 2010 an...
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Seven-day-ahead forecasting models of Cylindrospermopsis raciborskii in three warm-monomictic and mesotrophic reservoirs in south-east Queensland have been developed by means of water quality data from 1999 to 2010 and the hybrid evolutionary algorithm HEA. Resulting models using all measured variables as inputs as well as models using electronically measurable variables only as inputs forecasted accurately timing of overgrowth of C raciborskii and matched well high and low magnitudes of observed bloom events with 0.45 <= r(2) > 0.61 and 0.4 <= r(2) > 0.57, respectively. The models also revealed relationships and thresholds triggering bloom events that provide valuable information on synergism between water quality conditions and population dynamics of C. raciborskii. Best performing models based on using all measured variables as inputs indicated electrical conductivity (EC) within the range of 206-280 mS m(-1) as threshold above which fast growth and high abundances of C raciborskii have been observed for the three lakes. Best models based on electronically measurable variables for the Lakes Wivenhoe and Somerset indicated a water temperature (WT) range of 25.5-32.7 degrees C within which fast growth and high abundances of C raciborskii can be expected. By contrast the model for Lake Samsonvale highlighted a turbidity (TURB) level of 4.8 NTU as indicator for mass developments of C raciborskii. Experiments with online measured water quality data of the Lake Wivenhoe from 2007 to 2010 resulted in predictive models with 0.61 <= r(2) > 0.65 whereby again similar levels of EC and WT have been discovered as thresholds for outgrowth of C raciborskii. The highest validity of r(2) = 0.75 for an in situ data-based model has been achieved after considering time lags for EC by 7 days and dissolved oxygen by 1 day. These time lags have been discovered by a systematic screening of all possible combinations of time lags between 0 and 10 days for all electronically measurable var
Concept drift (CD) in data streams can significantly impact the performance and stability of data stream classification algorithms, diminishing the generalization capabilities of integrated learning models. This paper...
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Concept drift (CD) in data streams can significantly impact the performance and stability of data stream classification algorithms, diminishing the generalization capabilities of integrated learning models. This paper addresses CD issues in dichotomous data streams by introducing a novel modeling approach that leverages evolutionary computation techniques. The method entails grouping base learners based on their performance within a sliding window and then evolving the base learning periods using evolutionary techniques. Furthermore, the concept of "gene flow" is introduced to enhance diversity among base learners and improve CD prediction performance. Experimental results on real and artificial datasets demonstrate the superior comprehensive performance of the proposed method. Specifically, the BCDECA algorithm outperforms other similar methods, excelling in accuracy, diversity, convergence rate, and robustness on a range of datasets. Addresses CD issues in dichotomous data streams by introducing a novel modeling approach that leverages evolutionary computation *** base learners based on their performance within a sliding window and then evolving the base learning periods using evolutionary techniquesIntroduce the concept of "gene flow"
computational time and solution precision are two major concerns in evolutionary computation (EC). Although high-performance computing techniques have been applied to reduce the computational time of meta-heuristic al...
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computational time and solution precision are two major concerns in evolutionary computation (EC). Although high-performance computing techniques have been applied to reduce the computational time of meta-heuristic algorithms, it does not mean that they can assist meta-heuristic algorithms in finding a high-quality solution. Moreover, most of meta-heuristic algorithms may belong to the "depth-first" search method, thus achieving a tradeoff between the broad search and the deep search is a crucial objective in the EC. To alleviate the above problems, a parallel zoning search (PZS) strategy is proposed in the current study. In the PZS, the entire search space is divided into many small search spaces for improving the broad search capability of algorithms and reducing the problem complexity. Subsequently, selected meta-heuristic algorithms considered as deep search algorithms are employed to find a satisfactory solution in each search region. The effectiveness of the PZS integrated into six differential evolution (DE) variants is demonstrated on two commonly used test suites, i.e., IEEE CEC2014 and BBOB2012. Results suggest that the PZS is a highly competitive approach to solve different types of optimization problems, especially on complex optimization problems. Finally, the PZS incorporated into six DE variants is used to estimate parameters of a heavy oil thermal cracking model. Results indicate that the PZS is an effective and efficient tool to help selected algorithms solve actual industrial optimization problems.
As the field of evolutionary optimization continues to expand, it is becoming increasingly common to incorporate various machine learning approaches, such as clustering, classification, and regression models, to impro...
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As the field of evolutionary optimization continues to expand, it is becoming increasingly common to incorporate various machine learning approaches, such as clustering, classification, and regression models, to improve algorithmic efficiency. However, we note that although problem learning is popularly used in improving the ongoing optimization process, little effort is ever made in extracting re-usable domain knowledge. In other words, the acquired knowledge is seldom transferred and exploited for future design exercises. Focusing on evolutionary optimization, in this paper we investigate the concept of simultaneous problem learning and optimization inspired by the following notions: (1) that prior/dynamically acquired knowledge can enhance the effectiveness of evolutionary search, and (2) that evolution can be geared towards gathering crucial knowledge about the underlying problem. Taking benchmark functions as well as an engineering (process) design problem into consideration, we demonstrate the efficacy of a novel classifier-assisted constrained EA towards simultaneous evolutionary search and problem learning.
This paper proposes a method for generating 2-dimensional barcode incorporated some illustrations inside of the code without detracting machine-readability and stored information. We formulate the task that finding ap...
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This paper proposes a method for generating 2-dimensional barcode incorporated some illustrations inside of the code without detracting machine-readability and stored information. We formulate the task that finding appropriate positions, scales, and angles of illustrations, photographs, logos or other image items put in QR code as an optimization problem. By using evolutionary computation algorithm, the proposed system can find positions in which given image items can be merged without damaging machine-readability.
A novel approach for solving fuzzy model-based stability problems via evolutionary computation (EC) is presented. Gain scheduling problem of a multi-model fuzzy system that satisfies the Lyapunov stability criteria is...
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A novel approach for solving fuzzy model-based stability problems via evolutionary computation (EC) is presented. Gain scheduling problem of a multi-model fuzzy system that satisfies the Lyapunov stability criteria is solved. The generalized eigenvalue problem (GEVP) can be directly introduced to EC in searching positive definite (PD) or positive semi-definite (PSD) matrices, by making a penalty for an individual that violates the inequality condition in order to solve the nonlinear constraints or linear matrix inequalities (LMIs). Four examples for illustrating the proposed methodology are included and the results show the effectiveness. (C) 2003 Elsevier B.V. All rights reserved.
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