3,358 research outputs found
A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem
The 0-1 knapsack problem is a well-known combinatorial optimisation problem.
Approximation algorithms have been designed for solving it and they return
provably good solutions within polynomial time. On the other hand, genetic
algorithms are well suited for solving the knapsack problem and they find
reasonably good solutions quickly. A naturally arising question is whether
genetic algorithms are able to find solutions as good as approximation
algorithms do. This paper presents a novel multi-objective optimisation genetic
algorithm for solving the 0-1 knapsack problem. Experiment results show that
the new algorithm outperforms its rivals, the greedy algorithm, mixed strategy
genetic algorithm, and greedy algorithm + mixed strategy genetic algorithm
Mixed strategy may outperform pure strategy: An initial study
In pure strategy meta-heuristics, only one search strategy is applied for all
time. In mixed strategy meta-heuristics, each time one search strategy is
chosen from a strategy pool with a probability and then is applied. An example
is classical genetic algorithms, where either a mutation or crossover operator
is chosen with a probability each time. The aim of this paper is to compare the
performance between mixed strategy and pure strategy meta-heuristic algorithms.
First an experimental study is implemented and results demonstrate that mixed
strategy evolutionary algorithms may outperform pure strategy evolutionary
algorithms on the 0-1 knapsack problem in up to 77.8% instances. Then
Complementary Strategy Theorem is rigorously proven for applying mixed strategy
at the population level. The theorem asserts that given two meta-heuristic
algorithms where one uses pure strategy 1 and another uses pure strategy 2, the
condition of pure strategy 2 being complementary to pure strategy 1 is
sufficient and necessary if there exists a mixed strategy meta-heuristics
derived from these two pure strategies and its expected number of generations
to find an optimal solution is no more than that of using pure strategy 1 for
any initial population, and less than that of using pure strategy 1 for some
initial population
Magnetic moments and electromagnetic radii of nucleon and in an extended GBE model
We derive the exchange currents of pseudoscalar, vector, and scalar mesons
from Feynman diagrams, and use them to calculate the magnetic form factors of
nucleon and . The magnetic moments and electromagnetic radii are
obtained by using those form factors and the parameters determined from the
masses of nucleon and . We find the magnetic moments and
electromagnetic radii of nucleon and can be produced very well
in the extended Goldstone-boson-exchange model (GBE) in which all of
pseudoscalar, vector and scalar meson nonet are included. The magnetic moments
of are closer to experiment values and results from lattice
calculation than the results obtained by the model without other mesons except
for pion and sigma.Comment: 15 pages,5 figure
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