1,780 research outputs found
04081 Abstracts Collection -- Theory of Evolutionary Algorithms
From 15.02.04 to 20.02.04, the Dagstuhl Seminar 04081 ``Theory of Evolutionary Algorithms\u27\u27 was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
06061 Abstracts Collection -- Theory of Evolutionary Algorithms
From 05.02.06 to 10.02.06, the Dagstuhl Seminar 06061 ``Theory of Evolutionary Algorithms\u27\u27 was held in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Multi-objective improvement of software using co-evolution and smart seeding
Optimising non-functional properties of software is an important part of the implementation process. One such property is execution time, and compilers target a reduction in execution time using a variety of optimisation techniques. Compiler optimisation is not always able to produce semantically equivalent alternatives that improve execution times, even if such alternatives are known to exist. Often, this is due to the local nature of such optimisations. In this paper we present a novel framework for optimising existing software using a hybrid of evolutionary optimisation techniques. Given as input the implementation of a program or function, we use Genetic Programming to evolve a new semantically equivalent version, optimised to reduce execution time subject to a given probability distribution of inputs. We employ a co-evolved population of test cases to encourage the preservation of the program’s semantics, and exploit the original program through seeding of the population in order to focus the search. We carry out experiments to identify the important factors in maximising efficiency gains. Although in this work we have optimised execution time, other non-functional criteria could be optimised in a similar manner
NGS Based Haplotype Assembly Using Matrix Completion
We apply matrix completion methods for haplotype assembly from NGS reads to
develop the new HapSVT, HapNuc, and HapOPT algorithms. This is performed by
applying a mathematical model to convert the reads to an incomplete matrix and
estimating unknown components. This process is followed by quantizing and
decoding the completed matrix in order to estimate haplotypes. These algorithms
are compared to the state-of-the-art algorithms using simulated data as well as
the real fosmid data. It is shown that the SNP missing rate and the haplotype
block length of the proposed HapOPT are better than those of HapCUT2 with
comparable accuracy in terms of reconstruction rate and switch error rate. A
program implementing the proposed algorithms in MATLAB is freely available at
https://github.com/smajidian/HapMC
A 16kV Distribution Network Reconfiguration by Using Evolutionaring Programming for Loss Minimizing
In the worldwide trend toward restructuring the electricity network; there are a
lot of problems. Where with the increasing of electricity demand, intelligence
algorithm is one of the optimization search engine that may help in
minimizing the power losses in the power distribution network. This paper
presents a method of 16kV Distribution Network Reconfigurtion (DNR) by
using Evolutionary Programming (EP). The main objectives of this study are
to minimize the power losses and improve the voltage profile while analyzing
the consistency and computing time effectively. The performance of the
Evolutionary Programming method will be investigated and the impact to the
16kV distribution network will be analyzed. Thereal result will be compared
with the conventional initial network and other optimization technique which
is Genetic Algorithm (GA). The results of this study is hoped to help the
power system engineers in Malaysia in order to solve the losses problem in the
plant at the same time increasing the efficiency of the real 16-bus distribution
system
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