نتایج جستجو برای: probabilistic evolutionary

تعداد نتایج: 188737  

A new hybrid algorithm of Particle Swarm Optimization and Genetic Algorithm (PSOGA) is presented to get the optimum design of truss structures with discrete design variables. The objective function chosen in this paper is the total weight of the truss structure, which depends on upper and lower bounds in the form of stress and displacement limits. The Particle Swarm Optimization basically model...

In this paper, we give a kind of Cauchy 1-completeness in probabilistic quasi-uniform spaces by using 1-filters. Utilizingthe relationships among probabilistic quasi-uniformities, classical quasi-uniformities and Hutton [0, 1]-quasi-uniformities,we show the relationships between their completeness. In fuzzy quasi-metric spaces, we establish the relationshipsbetween the completeness of induced p...

Journal: :IEEE Trans. Evolutionary Computation 2002
Kuk-Hyun Han Jong-Hwan Kim

This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, the evaluation function, and the popul...

1999
Marcus Gallagher Marcus R. Frean Tom Downs

Population-Based Incremental Learning (PBIL) is an abstraction of a genetic algorithm , which solves optimization problems by explicitly constructing a probabilistic model of the promising regions of the search space. At each iteration the model is used to generate a population of candidate solutions and is itself modiied in response to these solutions. Through the extension of PBIL to Real-val...

2006
Teresa Miquélez Endika Bengoetxea Pedro Larrañaga

In this work, we present a generalisation to continuous domains of an optimization method based on evolutionary computation that applies Bayesian classifiers in the learning process. The main difference between other estimation of distribution algorithms (EDAs) and this new method –known as Evolutionary Bayesian Classifier-based Optimization Algorithms (EBCOAs)– is the way the fitness function ...

Journal: :HFSP journal 2009
Oliver Ratmann Carsten Wiuf John W Pinney

The evolutionary mechanisms by which protein interaction networks grow and change are beginning to be appreciated as a major factor shaping their present-day structures and properties. Starting with a consideration of the biases and errors inherent in our current views of these networks, we discuss the dangers of constructing evolutionary arguments from naïve analyses of network topology. We ar...

2006
Simon Laird Henrik Jeldtoft Jensen

We use a generalised version of the individual-based Tangled Nature model of evolutionary ecology to study the relationship between ecosystem structure and evolutionary history. Our evolved model ecosystems typically exhibit interaction networks with exponential degree distributions and an inverse dependence between connectance and species richness. We use a simplified network evolution model t...

Journal: :Philosophical Transactions of the Royal Society B: Biological Sciences 2008
Ari Löytynoja Nick Goldman

We have developed a phylogeny-aware progressive alignment method that recognizes insertions and deletions as distinct evolutionary events and thus avoids systematic errors created by traditional alignment methods. We now extend this method to simultaneously model regional heterogeneity and evolution. This novel method can be flexibly adapted to alignment of nucleotide or amino acid sequences ev...

Journal: :Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 2004
Alan M. Moses Derek Y. Chiang Michael B. Eisen

The preferential conservation of transcription factor binding sites implies that non-coding sequence data from related species will prove a powerful asset to motif discovery. We present a unified probabilistic framework for motif discovery that incorporates evolutionary information. We treat aligned DNA sequence as a mixture of evolutionary models, for motif and background, and, following the e...

2005
Ramesh Rajagopalan Chilukuri K. Mohan Kishan G. Mehrotra Pramod K. Varshney

A new evolutionary multi-objective crowding algorithm (EMOCA) is evaluated using nine benchmark multiobjective optimization problems, and shown to produce non-dominated solutions with significant diversity, outperforming state-of-the-art multi-objective evolutionary algorithms viz., Non-dominated Sorting Genetic Algorithm – II (NSGA-II), Strength Pareto Evolutionary algorithm II (SPEA-II) and P...

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