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Experimental analysis of the relevance of fitness landscape topographical characterization
2012 IEEE Congress on Evolutionary Computation, 2012The performance of any Evolutionary Algorithm (EA) is closely related to the topographical features of the problem fitness landscape it is applied to. It is therefore of paramount importance to determine a set of features that is useful in order to choose an appropriate algorithm for a given problem. This way, the inefficient trial and error stage that
Pilar Caamaño +4 more
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Fitness Landscape Analysis of Circles in a Square Packing Problems
2014Fitness landscape analysis provides insight into the structural features of optimization problems. Landscape analysis techniques have been individually shown to capture specific continuous landscape features. However, results are typically for benchmark and artificial problems, and so the ability of techniques to capture real-world problem structures ...
Rachael Morgan, Marcus Gallagher
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Correlation Analysis of Fitness Landscapes. [PDF]
, 1999 Fitness landscapes underlie the dynamics of evolutionary processes and are a key concept of evolutionary theory. Recent research on molecular folding and on evolutionary algorithms has demonstrated that such landscapes are also important for understanding problems of chemistry and of combinatorial optimization.
H. Brandt, U. Dieckmann
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Complex network analysis in PSO as an fitness landscape classifier
2016 IEEE Congress on Evolutionary Computation (CEC), 2016In this paper, an initial small-scale study is carried out. It is proposed that using the complex network analysis it may be possible to make a classification of the fitness landscape type. A complex network is constructed from the inner dynamics of the population in PSO algorithm.
Michal Pluhacek +4 more
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Advanced Fitness Landscape Analysis and the Performance of Memetic Algorithms
Evolutionary Computation, 2004Memetic algorithms (MAs) have demonstrated very effective in combinatorial optimization. This paper offers explanations as to why this is so by investigating the performance of MAs in terms of efficiency and effectiveness. A special class of MAs is used to discuss efficiency and effectiveness for local search and evolutionary meta-search.
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A Comprehensive Survey on Fitness Landscape Analysis
2012In the past, the notion of fitness landscapes has found widespread adoption. Many different methods have been developed that provide a general and abstract framework applicable to any optimization problem. We formally define fitness landscapes, provide an in-depth look at basic properties and give detailed explanations and examples of existing fitness ...
Erik Pitzer, Michael Affenzeller
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Dynamic Fitness Landscape Analysis
2013Solving optimization problems with time varying objective functions by methods of evolutionary computation can be grounded on the theoretical framework of dynamic fitness landscapes. In this chapter, we define such dynamic fitness landscapes and discuss their properties.
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Fitness landscape analysis for evolutionary non-photorealistic rendering
IEEE Congress on Evolutionary Computation, 2010The best evolutionary approach can be a difficult problem. In this work we have investigated two evolutionary representations to evolve non-photorealistic renderings: a variable-length classic genetic algorithm representation, and a tree-based genetic algorithm representation.
Jeff Riley, Victor Ciesielski
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Fitness Landscape Analysis in the Optimization of Coefficients of Curve Parametrizations
2018Parametric representations of geometric objects, such as curves or surfaces, may have unnecessarily huge integer coefficients. Our goal is to search for an alternative parametric representation of the same object with significantly smaller integer coefficients.
Stephan M. Winkler, J. Rafael Sendra
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