8,234 research outputs found
Self-adaptation of mutation distribution in evolutionary algorithms
This paper is posted here with permission from IEEE - Copyright @ 2007 IEEEThis paper proposes a self-adaptation method to control not only the mutation strength parameter, but also the mutation distribution for evolutionary algorithms. For this purpose, the isotropic g-Gaussian distribution is employed in the mutation operator. The g-Gaussian distribution allows to control the shape of the distribution by setting a real parameter g and can reproduce either finite second moment distributions or infinite second moment distributions. In the proposed method, the real parameter q of the g-Gaussian distribution is encoded in the chromosome of an individual and is allowed to evolve. An evolutionary programming algorithm with the proposed idea is presented. Experiments were carried out to study the performance of the proposed algorithm
Use of the q-Gaussian mutation in evolutionary algorithms
Copyright @ Springer-Verlag 2010.This paper proposes the use of the q-Gaussian mutation with self-adaptation of the shape of the mutation distribution in evolutionary algorithms. The shape of the q-Gaussian mutation distribution is controlled by a real parameter q. In the proposed method, the real parameter q of the q-Gaussian mutation is encoded in the chromosome of individuals and hence is allowed to evolve during the evolutionary process. In order to test the new mutation operator, evolution strategy and evolutionary programming algorithms with self-adapted q-Gaussian mutation generated from anisotropic and isotropic distributions are presented. The theoretical analysis of the q-Gaussian mutation is also provided. In the experimental study, the q-Gaussian mutation is compared to Gaussian and Cauchy mutations in the optimization of a set of test functions. Experimental results show the efficiency of the proposed method of self-adapting the mutation distribution in evolutionary algorithms.This work was supported in part by FAPESP and CNPq in Brazil and in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant EP/E060722/1 and Grant EP/E060722/2
Effective Fitness Landscapes for Evolutionary Systems
In evolution theory the concept of a fitness landscape has played an
important role, evolution itself being portrayed as a hill-climbing process on
a rugged landscape. In this article it is shown that in general, in the
presence of other genetic operators such as mutation and recombination,
hill-climbing is the exception rather than the rule. This descrepency can be
traced to the different ways that the concept of fitness appears --- as a
measure of the number of fit offspring, or as a measure of the probability to
reach reproductive age. Effective fitness models the former not the latter and
gives an intuitive way to understand population dynamics as flows on an
effective fitness landscape when genetic operators other than selection play an
important role. The efficacy of the concept is shown using several simple
analytic examples and also some more complicated cases illustrated by
simulations.Comment: 11 pages, 8 postscript figure
Inverse relationship between genetic diversity and epigenetic complexity
Early studies of molecular evolution revealed a correlation between genetic distance and time of species divergence. This observation provoked the molecular clock hypothesis and in turn the ‘Neutral Theory’, which however remains an incomplete explanation since it predicts a constant mutation rate per generation whereas empirical evidence suggests a constant rate per year. Data inconsistent with the molecular clock hypothesis have steadily accumulated in recent years that show no correlation between genetic distance and time of divergence. It has therefore become a challenge to find a testable idea that can reconcile the seemingly conflicting data sets. Here, an inverse relationship between genetic diversity and epigenetic complexity was deduced from a simple intuition in building complex systems. Genetic diversity, i.e., genetic distance or dissimilarity in DNA or protein sequences between individuals or species, is restricted by the complexity of epigenetic programs. This inverse relationship logically deduces the maximum genetic diversity hypothesis, which suggests that macroevolution from simple to complex organisms involves a punctuational increase in epigenetic complexity that in turn causes a punctuational loss in genetic diversity. The hypothesis explains a diverse set of biological phenomena, including both for and against the correlation between genetic distance and time of divergence.

Formation of regulatory modules by local sequence duplication
Turnover of regulatory sequence and function is an important part of
molecular evolution. But what are the modes of sequence evolution leading to
rapid formation and loss of regulatory sites? Here, we show that a large
fraction of neighboring transcription factor binding sites in the fly genome
have formed from a common sequence origin by local duplications. This mode of
evolution is found to produce regulatory information: duplications can seed new
sites in the neighborhood of existing sites. Duplicate seeds evolve
subsequently by point mutations, often towards binding a different factor than
their ancestral neighbor sites. These results are based on a statistical
analysis of 346 cis-regulatory modules in the Drosophila melanogaster genome,
and a comparison set of intergenic regulatory sequence in Saccharomyces
cerevisiae. In fly regulatory modules, pairs of binding sites show
significantly enhanced sequence similarity up to distances of about 50 bp. We
analyze these data in terms of an evolutionary model with two distinct modes of
site formation: (i) evolution from independent sequence origin and (ii)
divergent evolution following duplication of a common ancestor sequence. Our
results suggest that pervasive formation of binding sites by local sequence
duplications distinguishes the complex regulatory architecture of higher
eukaryotes from the simpler architecture of unicellular organisms
An Evolutionary Algorithm for the Estimation of Threshold Vector Error Correction Models
We develop an evolutionary algorithm to estimate Threshold Vector Error Correction models (TVECM) with more than two cointegrated variables. Since disregarding a threshold in cointegration models renders standard approaches to the estimation of the cointegration vectors inefficient, TVECM necessitate a simultaneous estimation of the cointegration vector(s) and the threshold. As far as two cointegrated variables are considered this is commonly achieved by a grid search. However, grid search quickly becomes computationally unfeasible if more than two variables are cointegrated. Therefore, the likelihood function has to be maximized using heuristic approaches. Depending on the precise problem structure the evolutionary approach developed in the present paper for this purpose saves 90 to 99 per cent of the computation time of a grid search.evolutionary strategy, genetic algorithm, TVECM
Using Self-Adaptive Evolutionary Algorithms to Evolve Dynamism-Oriented Maps for a Real Time Strategy Game
9th International Conference on Large Scale Scientific Computations. The final publication is available at link.springer.comThis work presents a procedural content generation system that uses an evolutionary algorithm in order to generate interesting maps for a real-time strategy game, called Planet Wars. Interestingness is here captured by the dynamism of games (i.e., the extent to which they are action-packed). We consider two different approaches to measure the dynamism of the games resulting from these generated maps, one based on fluctuations in the resources controlled by either player and another one based on their confrontations. Both approaches rely on conducting several games on the map under scrutiny using top artificial intelligence (AI) bots for the game. Statistic gathered during these games are then transferred to a fuzzy system that determines the map's level of dynamism. We use an evolutionary algorithm featuring self-adaptation of mutation parameters and variable-length chromosomes (which means maps of different sizes) to produce increasingly dynamic maps.TIN2011-28627-C04-01, P10-TIC-608
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