For several medical treatments, it is possible to observe transcriptional variations in gene expressions between responders and non-responders. Modelling the correlation between such variations and the patient's r...
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ISBN:
(纸本)9783030726980;9783030726997
For several medical treatments, it is possible to observe transcriptional variations in gene expressions between responders and non-responders. Modelling the correlation between such variations and the patient's response to drugs as a system of Ordinary Differential Equations could be invaluable to improve the efficacy of treatments and would represent an important step towards personalized medicine. Two main obstacles lie on this path: (i) the number of genes is too large to straightforwardly analyze their interactions;(ii) defining the correct parameters for the mathematical models of gene interaction is a complex optimization problem, even when a limited number of genes is involved. In this paper, we propose a novel approach to creating mathematical models able to explain patients' response to treatment from transcriptional variations. The approach is based on: (i) a feature selection algorithm, set to identify a minimal set of gene expressions that are highly correlated with treatment outcome, (ii) a state-of-the-art evolutionary optimizer, Covariance Matrix Adaptation Evolution Strategy, applied to finding the parameters of the mathematical model characterizing the relationship between gene expressions and patient responsiveness. The proposed methodology is tested on real-world data describing responsiveness of asthma patients to Omalizumab, a humanized monoclonal antibody that binds to immunoglobulin E. In this case study, the presented approach is shown able to identify 5 genes (out of 28,402) that are transcriptionally relevant to predict treatment outcomes, and to deliver a compact mathematical model that is able to explain the interaction between the different genes involved.
In this paper, an evolutionary algorithm is applied to tackle Intelligent Curriculum Sequencing issue. The purpose is to align educational technology, for instance, curriculum sequence to: students' characteristic...
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ISBN:
(纸本)9781509007516
In this paper, an evolutionary algorithm is applied to tackle Intelligent Curriculum Sequencing issue. The purpose is to align educational technology, for instance, curriculum sequence to: students' characteristics and subject-matter coherence. The algorithm considers both technical and pedagogical point of view. Results show that the proposed evolutionary computation Search Algorithm could find optimal learning sequences, under a set of constraints, within a reasonable amount of iterations.
We propose a novel population-based optimization algorithm, Chaotic Evolution (CE), that uses a chaotic ergodicity to implement exploitation and exploration functions of the evolutionary computation algorithm. A new c...
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ISBN:
(纸本)9781467347143
We propose a novel population-based optimization algorithm, Chaotic Evolution (CE), that uses a chaotic ergodicity to implement exploitation and exploration functions of the evolutionary computation algorithm. A new control parameter, direction factor rate, is proposed in CE to guide search direction. Compared with differential evolution (DE), our proposal works with the more simple principle, and can obtain the better optimization performance, escape from the local optimum and avoid the premature. By changing the chaotic system in our proposal, it is easy to extend its search capability, i.e., the scalability of our proposal is higher than DE. A series of comparative evaluations are conducted to analyze the feature of the proposal. From these results and analysis, our proposed algorithm can optimize most of benchmark functions and outperforms better than DE.
In our work we present a basic outline of information retrieval process using evolutionary computation and some of the basic models which are being employed. Different evolutionary techniques like particle swarm optim...
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ISBN:
(纸本)9781509064717
In our work we present a basic outline of information retrieval process using evolutionary computation and some of the basic models which are being employed. Different evolutionary techniques like particle swarm optimization, ant colony optimization, and genetic algorithm are used for optimizing the data using different sets of algorithms which are comparatively better than traditional computing techniques. It covers a brief overview of evolutionary computation and how optimization helps to retrieve better results using different techniques.
Due to increasing complexity and Don-convexity of financial engineering problems, biologically inspired heuristic algorithms gained significant importance especially in the area of financial decision optimization. In ...
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ISBN:
(纸本)9783540718048
Due to increasing complexity and Don-convexity of financial engineering problems, biologically inspired heuristic algorithms gained significant importance especially in the area of financial decision optimization. In this paper, the stochastic scenario-based risk-return portfolio optimization problem is analyzed and solved with an evolutionary computation approach. The advantage of applying this approach is the creation of a common framework for an arbitrary set of loss distribution-based risk measures, regardless of their underlying structure. Numerical results for three of the most commonly used risk measures conclude the paper.
Software product lines are an excellent mechanism in the development of software. Testing software product lines is an intensive process where selecting the right features where to focus it can be a critical task. Sel...
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ISBN:
(纸本)9781728169293
Software product lines are an excellent mechanism in the development of software. Testing software product lines is an intensive process where selecting the right features where to focus it can be a critical task. Selecting the best combination of features from a software product line is a complex problem addressed in the literature. In this paper, we address the problem of finding the combination of features with the highest probability of being requested from a software product line with probabilities. We use Evolutive computation techniques to address this problem. Specifically, we use the Ant Colony Optimization algorithm to find the best combination of features. Our results report that our framework overcomes the limitations of the brute force algorithm.
A deceptive problem with known analytical solution is introduced. Arguably its solution search landscape is such that heuristic methods will find it difficult to search for the solution. The problem is tunable offerin...
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ISBN:
(纸本)9781728104041
A deceptive problem with known analytical solution is introduced. Arguably its solution search landscape is such that heuristic methods will find it difficult to search for the solution. The problem is tunable offering a test bed by which to examine the performance of different methods of heuristic and evolutionary search.
In this paper, we propose a general idea of Cellular evolutionary computation (CEC). CEC is evolutionary computation that solves the optimization problems with real DNA molecules and cells. The easiest means of cellul...
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