The past two decades have witnessed great advances in the computational modeling and systems biology fields. Soon after the first models of metabolism were developed, methods for phenotype prediction were put forward,...
详细信息
ISBN:
(纸本)9781450349390
The past two decades have witnessed great advances in the computational modeling and systems biology fields. Soon after the first models of metabolism were developed, methods for phenotype prediction were put forward, as well as strain optimization methods, within the field of Metabolic Engineering. evolutionary computation has been on the front line, with the proposal of bilevel metaheuristics, where EC works over phenotype simulation, selecting the most promising solutions for bioengineering tasks. Recently, Schuetz and co-workers proposed that the metabolism of bacteria operates close to the Pareto-optimal surface of a three-dimensional space defined by competing objectives. Albeit multi-objective strain optimization approaches focused on bioengineering objectives have been proposed, none tackles the multiobjective nature of the cellular objectives. In this work, we propose multi-objective evolutionary algorithms for strain optimization, where objective functions are defined based on distinct phenotype prediction methods;showing that those can lead to more robust designs, allowing to find solutions in more complex scenarios.
This paper describes the application of a Differential Evolution based approach for inducing oblique decision trees in a recursive partitioning strategy. Considering that: (1) the task of finding an optimal hyperplane...
详细信息
ISBN:
(纸本)9783319590639;9783319590622
This paper describes the application of a Differential Evolution based approach for inducing oblique decision trees in a recursive partitioning strategy. Considering that: (1) the task of finding an optimal hyperplane with real-valued coefficients is a complex optimization problem in a continuous space, and (2) metaheuristics have been successfully applied for solving this type of problems, in this work a differential evolution algorithm is applied with the aim of finding near-optimal hyperplanes that will be used as test conditions of an oblique decision tree. The experimental results show that this approach induces more accurate classifiers than those produced by other proposed induction methods.
An innovative massive multi-objective design procedure is proposed for the synthesis of next-generation antennas for 5G base stations. The 5G antenna design problem is formulated by jointly considering several contras...
详细信息
ISBN:
(纸本)9788890701870
An innovative massive multi-objective design procedure is proposed for the synthesis of next-generation antennas for 5G base stations. The 5G antenna design problem is formulated by jointly considering several contrasting requirements in terms of bandwidth, directivity, half-power beamwidth, polarization, and neighbor element isolation. Towards this end, a finite-array model is developed which enables the simulation of a set of adjacent elements during the design process. Thanks to such an approach, the obtained design can be directly included in 5G antenna arrays without further re-optimization to compensate for mutual coupling effects. The resulting massive multi-objective problem is recast as a multi-objective one by suitably clustering the cost function terms according to their physical features, and ad-hoc global search techniques are customized and applied in order to address with the obtained highly non-linear optimization problem. Preliminary numerical results concerning a Paretooptimal tradeoff solution are presented to validate the proposed approach.
Purpose: This work presents a task-driven joint optimization of fluence field modulation (FFM) and regularization in quadratic penalized-likelihood (PL) reconstruction. Conventional FFM strategies proposed for filtere...
详细信息
ISBN:
(数字)9781510607101
ISBN:
(纸本)9781510607095;9781510607101
Purpose: This work presents a task-driven joint optimization of fluence field modulation (FFM) and regularization in quadratic penalized-likelihood (PL) reconstruction. Conventional FFM strategies proposed for filtered-backprojection (FBP) are evaluated in the context of PL reconstruction for comparison. Methods: We present a task-driven framework that leverages prior knowledge of the patient anatomy and imaging task to identify FFM and regularization. We adopted a maxi-min objective that ensures a minimum level of detectability index (d') across sample locations in the image volume. The FFM designs were parameterized by 2D Gaussian basis functions to reduce dimensionality of the optimization and basis function coefficients were estimated using the covariance matrix adaptation evolutionary strategy (CMA-ES) algorithm. The FFM was jointly optimized with both space-invariant and spatially-varying regularization strength (beta) - the former via an exhaustive search through discrete values and the latter using an alternating optimization where beta was exhaustively optimized locally and interpolated to form a spatially-varying map. Results: The optimal FFM inverts as beta increases, demonstrating the importance of a joint optimization. For the task and object investigated, the optimal FFM assigns more fluence through less attenuating views, counter to conventional FFM schemes proposed for FBP. The maxi-min objective homogenizes detectability throughout the image and achieves a higher minimum detectability than conventional FFM strategies. Conclusions: The task-driven FFM designs found in this work are counter to conventional patterns for FBP and yield better performance in terms of the maxi-min objective, suggesting opportunities for improved image quality and/or dose reduction when model-based reconstructions are applied in conjunction with FFM.
Dynamical system is a mathematical approach to model the nonlinear dynamics of complex systems over space and time. A causality-informed genetic programming (GP) ensemble methodology has been proposed recently by the ...
详细信息
ISBN:
(纸本)9781450363396
Dynamical system is a mathematical approach to model the nonlinear dynamics of complex systems over space and time. A causality-informed genetic programming (GP) ensemble methodology has been proposed recently by the authors to automatically infer dynamical systems from system observations. The method adopts a variable decomposition approach relies on expert defined causal models. However, in practice these models are bound to have inconsistencies due to human involvement. Hence, in this paper we evaluate the sensitivity of the ensemble method to the accuracy of input causal models that are used as ground truth in the formation of the ensemble. This is done by varying the accuracy of known causal models through introducing deliberate noise in models' causal relationships. Three benchmark problems are used to evaluate the performance of the proposed methodology where the output of different ensembles is compared with a standard GP algorithm. The empirical results show the effectiveness of the proposed methodology in inferring closely matching target equations under different levels of noise and learning better models than the standard GP algorithm in most cases.
Differential Evolution (DE) has been considered as an effective approach for solving numerical optimization problems. Due to different characteristics of optimization problems, many proposed algorithms try to perform ...
详细信息
ISBN:
(纸本)9781538605271
Differential Evolution (DE) has been considered as an effective approach for solving numerical optimization problems. Due to different characteristics of optimization problems, many proposed algorithms try to perform consistently over a range of problems. The proposed algorithm combines both LSHADE and MTS-LS1 by giving both a participation ratio of the fitness evaluation where each technique works until finishing its participation budget or reaching the optimum solution. Using hybrid model gives an opportunity to achieve better performance for both algorithms. The evaluation of this algorithm has been tested using CEC 2014 benchmark problems.
Optimization algorithms, especially evolutionary algorithms, have gained wide acceptance among many disciplines such as electrical, control or industrial engineering. The ability to solve an objective or cost function...
详细信息
ISBN:
(纸本)9781538617236
Optimization algorithms, especially evolutionary algorithms, have gained wide acceptance among many disciplines such as electrical, control or industrial engineering. The ability to solve an objective or cost function with more unknown parameters than known equations, which make the problem unsolvable by means of deterministic approaches, is the main benefit of using evolutionary algorithms. In all cases optimization algorithms rely on starting with a completely random initial solution set then evolves this set towards better ones in respect to fitness or objective function iteratively. In this study, we have proven that starting from a unique point after a brief local and deterministic search instead of a pure random set is more beneficial in respect to fitness function evaluation count, or computation time. This approach, although it can be applied to any optimization algorithm, is a natural add-on to Big Bang - Big Crunch (BBBC) optimization method.
Metaheuristics for optimization based on the immune network theory are often highlighted by being able to maintain the diversity of candidate solutions present in the population, allowing a greater coverage of the sea...
详细信息
ISBN:
(纸本)9781450349208
Metaheuristics for optimization based on the immune network theory are often highlighted by being able to maintain the diversity of candidate solutions present in the population, allowing a greater coverage of the search space. This work, however, shows that algorithms derived from the aiNET family for the solution of combinatorial problems may not present an adequate strategy for search space exploration, leading to premature convergence in local minimums. In order to solve this issue, a hybrid metaheuristic called VNS-aiNET is proposed, integrating aspects of the COPT-aiNET algorithm with characteristics of the trajectory metaheuristic Variable Neighborhood Search (VNS), as well as a new fitness function, which makes it possible to escape from local minima and enables it to a greater exploration of the search space. The proposed metaheuristic is evaluated using a scheduling problem widely studied in the literature. The performed experiments show that the proposed hybrid metaheuristic presents a convergence superior to two approaches of the aiNET family and to the reference algorithms of the literature. In contrast, the solutions present in the resulting immunological memory have less diversity when compared to the aiNET family approaches.
The concept of nonlinguistic information includes all types of extra linguistic information such as factors of age, emotion and physical states, accent and others. Semi-supervised techniques based on using both labell...
详细信息
ISBN:
(纸本)9789897583049
The concept of nonlinguistic information includes all types of extra linguistic information such as factors of age, emotion and physical states, accent and others. Semi-supervised techniques based on using both labelled and unlabelled examples can be an efficient tool for solving nonlinguistic information extraction problems with large amounts of unlabelled data. In this paper a new cooperation of biology related algorithms (COBRA) for semi-supervised support vector machines (SVM) training and a new self-configuring genetic algorithm (SelfCGA) for the automated design of semi-supervised artificial neural networks (ANN) are presented. Firstly, the performance and behaviour of the proposed semi-supervised SVMs and semi-supervised ANNs were studied under common experimental settings;and their workability was established. Then their efficiency was estimated on a speech-based emotion recognition problem.
In this paper, we study the impact of using a hybrid-technique approach, which is a combination of genetic algorithm (GA) and protein's free energy minimization calculations, to predict protein tertiary structure....
详细信息
ISBN:
(纸本)9781538611913
In this paper, we study the impact of using a hybrid-technique approach, which is a combination of genetic algorithm (GA) and protein's free energy minimization calculations, to predict protein tertiary structure. We compare the results with a basic approach which applies genetic algorithm only. A genetic algorithm is used to predict the protein structure using the primary structure, the amino acids sequence of a given polypeptide chain, as input. After that, we combine the GA with energy minimization feature. Finally, the outcomes of both experiments are analyzed. Results reveal that the hybrid approach outperforms the basic one.
暂无评论