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Browsing by Subject "Evolutionary computation"
Now showing items 21-40 of 48
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(Springer, 2011-04-27)Representation is a very important component of any evolutionary algorithm. Changing the representation can cause an algorithm to perform very differently. Such a change can have an effect that is difficult to understand. ...
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(ACM, 2011-07-12)This paper examines how the semantic locality of a search operator affects the fitness landscape of Genetic Programming (GP). We compare the fitness landscapes of GP search when standard subtree mutation and a recently ...
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(Springer, 2011-04-27)There have been many approaches to modularity in the field of evolutionary computation, each tailored to function with a particular representation. This research examines one approach to modularity and grammar modification ...
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(Springer, 2012)The last ten years has seen the introduction and rapid growth of a market in weather derivatives, financial instruments whose payoffs are determined by the outcome of an underlying weather metric. These instruments allow ...
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(ACM, 2011-07-12)The application of a genotype-phenotype mapping in Evolutionary Computation is not a new idea, however, how this mapping process is interpreted, and implemented varies wildly. In the majority of cases a very simple abstraction ...
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(ACM, 2008)We are delighted to announce the release of GEVA an open source software implementation of Grammatical Evolution (GE) in Java. Grammatical Evolution in Java (GEVA) was developed at UCD’s Natural Computing Research & ...
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(Springer, 2010-05-01)Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP). Practical ...
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(IEEE, 2010-07)The use of higher-order functions, as a method of abstraction and re-use in EC encodings, has been the subject of relatively little research. In this paper we introduce and give motivation for the ideas of higher-order ...
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(IEEE, 2010-07)Locality - how well neighbouring genotypes correspond to neighbouring phenotypes - has been described as a key element in Evolutionary Computation. Grammatical Evolution (GE) is a generative system as it uses grammar rules ...
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Improving the generalisation ability of genetic programming with semantic similarity based crossover (Springer, 2010)This paper examines the impact of semantic control on the ability of Genetic Programming (GP) to generalise via a semantic based crossover operator (Semantic Similarity based Crossover - SSC). The use of validation sets ...
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(MIT Press, 2010)Perhaps the biggest limitation of interactive EC is the fitness evaluation bottleneck, caused by slow user evaluation and leading to small populations and user fatigue. In this study these problems are addressed through ...
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(IEEE, 2010-07)Interactive interpolating crossover allows a user to quickly see a large number of individuals formed by interactively-controlled interpolation between two or more parents. We study it here for the first time in the context ...
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(University College Dublin. School of Computer Science and Informatics, 2011-04-12)In this paper we explore different techniques that allow the user to direct interactive evolutionary search. Broadening interaction beyond simple evaluation increases the amount of feedback and bias a user can apply to the ...
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(IEEE, 2010-07)We present an investigation into the genotype-phenotype map in Position Independent Grammatical Evolution (πGE). Previous studies have shown πGE to exhibit a performance increase over standard GE. The only difference between ...
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(Springer, 2011-04-27)We present an analysis of how the genotype-phenotype map in Grammatical Evolution (GE) can effect performance on the Max Problem. Earlier studies have demonstrated a performance decrease for Position independent Grammatical ...
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(ACM, 2011-07-12)Modularity has proven to be an important aspect of evolutionary computation. This work is concerned with discovering and using modules in one form of grammar-based genetic programming, grammatical evolution (GE). Previous ...
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(IEEE Press, 2010-07)Designing a suitable objective function is an essential step in successfully applying an evolutionary algorithm to a problem. In this study we apply a grammar-based Genetic Programming algorithm called Grammatical Evolution ...
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(Springer, 2010-09)It is approximately 50 years since the first computational experiments were conducted in what has become known today as the field of Genetic Programming (GP), twenty years since John Koza named and popularised the method, ...
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(Springer, 2011-04-27)This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model overtraining, ...
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(Springer, 2011-04)This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model overtraining, ...
Now showing items 21-40 of 48