Open ended evolution seeks computational structures whereby creation of unbounded diversity and novelty are possible. However, research has run into a problem known as the "novelty plateau" where further cre...
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ISBN:
(纸本)9783319912530;9783319912523
Open ended evolution seeks computational structures whereby creation of unbounded diversity and novelty are possible. However, research has run into a problem known as the "novelty plateau" where further creation of novelty is not observed. Using standard algorithmic information theory and Chaitin's Incompleteness Theorem, we prove no algorithm can detect unlimited novelty. Therefore observation of unbounded novelty in computer evolutionary programs is nonalgorithmic and, in this sense, unknowable.
When evolutionary algorithms (EAs) are unlikely to locate precise global optimal solutions with satisfactory performances, it is important to substitute alternative theoretical routine for the analysis of hitting time...
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We describe initial results obtained when applying different multi-objective evolutionary algorithms (MOEAs) to direct topology optimization (DTO) scenarios that are relevant in the field of electrical machine design....
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ISBN:
(纸本)9783319747187;9783319747170
We describe initial results obtained when applying different multi-objective evolutionary algorithms (MOEAs) to direct topology optimization (DTO) scenarios that are relevant in the field of electrical machine design. Our analysis is particularly concerned with investigating if the use of discrete or real-value encodings combined with a preference for a particular population initialization strategy can have a severe impact on the performance of MOEAs applied for DTO.
Motion blur is a common blur type that created due to motion of camera or some objects in scene. The blur kernel, i.e. the function that simulate the motion blur process, depends on the length of blur. Therefore, the ...
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ISBN:
(纸本)9781538649787
Motion blur is a common blur type that created due to motion of camera or some objects in scene. The blur kernel, i.e. the function that simulate the motion blur process, depends on the length of blur. Therefore, the estimation of blur length is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e. image deblurring. In this paper, a method is proposed for estimation of the motion blur length using the evolutionary methods. To do this, we take the advantage of the relation between a blur metric, called NIDCT, and the blur length. Then this relation is learned via the evolutionary algorithms. The learned relation can be used to estimate the motion blur length in a blurred image. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.
Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automate...
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ISBN:
(纸本)9781450356381
Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive automotive system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that are likely to lead to system failures.
Diversity is an important factor in evolutionary algorithms to prevent premature convergence towards a single local optimum. In order to maintain diversity throughout the process of evolution, various means exist in l...
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ISBN:
(纸本)9781450356183
Diversity is an important factor in evolutionary algorithms to prevent premature convergence towards a single local optimum. In order to maintain diversity throughout the process of evolution, various means exist in literature. We analyze approaches to diversity that (a) have an explicit and quantifiable influence on fitness at the individual level and (b) require no (or very little) additional domain knowledge such as domain-specific distance functions. We also introduce the concept of genealogical diversity in a broader study. We show that employing these approaches can help evolutionary algorithms for global optimization in many cases.
Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in solving unknown problems due to no f...
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As evolutionary algorithms (EAs) are general-purpose optimization algorithms, recent theoretical studies have tried to analyze their performance for solving general problem classes, with the goal of providing a genera...
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evolutionary algorithm (EA) has been well accepted as a suitable tool for solving non-convex economic dispatch problems. However the major challenge is how to handle both equality and inequality constraints properly. ...
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ISBN:
(纸本)9781538623176
evolutionary algorithm (EA) has been well accepted as a suitable tool for solving non-convex economic dispatch problems. However the major challenge is how to handle both equality and inequality constraints properly. The penalized fitness is commonly used to evaluate quality of candidate solutions. Moreover searching for feasible space is very difficult for a problem with large number of variables and number of constraints. This paper proposes a general framework which can be applied to any EA for handling constraints in ED problems. Four EAs namely differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and harmony search (HS) were selected to demonstrate effectiveness in solving a non-convex ED problem of a 15 unit power system. Simulation results reveal that with the proposed constraint handling all optimization algorithms converge to the identical result based on 20 independent trials.
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