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Revisiting lagrange relaxation (LR) for processing large-scale mixed integer programming (MIP) problems
Lagrangean Relaxation has been successfully applied to process many well known
instances of NP-hard Mixed Integer Programming problems. In this paper we present
a Lagrangean Relaxation based generic solver for processing Mixed Integer
Programming problems. We choose the constraints, which are relaxed using a
constraint classification scheme. The tactical issue of updating the Lagrange
multiplier is addressed through sub-gradient optimisation; alternative rules for
updating their values are investigated. The Lagrangean relaxation provides a lower
bound to the original problem and the upper bound is calculated using a heuristic
technique. The bounds obtained by the Lagrangean Relaxation based generic solver
were used to warm-start the Branch and Bound algorithm; the performance of the
generic solver and the effect of the alternative control settings are reported for a wide
class of benchmark models. Finally, we present an alternative technique to calculate
the upper bound, using a genetic algorithm that benefits from the mathematical
structure of the constraints. The performance of the genetic algorithm is also
presented
Optimum design of a probe fed dual frequency patch antenna using genetic algorithm
Abstract: Recent research has concentrated on different designs in order to increase the bandwidth of patch antennas and thus improve functionality of wireless communication systems. An alternative approach as shown in this paper is to design a matched probe fed rectangular patch antenna which can operate at both dual frequency (1.9 GHz and 2.4 GHz) and dual polarisation. In this design there are four variables, the two dimensions of the rectangular patch, ‘a ’ and ‘b ’ and position of the probe feed ‘Xp ’ and ‘YP’. As there is not a unique solution Genetic Algorithm (GA) was applied using two objective functions for the return loss at each frequency. The antenna was then modelled using AWR software and the predicted and practical results are shown to be in good agreement. Key Words: Genetic algorithm (GA), dual frequency, dual polarisation, probe fed patch antenn
An alternative measurement of the entropy evolution of a genetic algorithm
This is an electronic version of the paper presented at The European Simulation and Modelling Conference (ESM), held in Leicester (United Kingdom) on 2009In a genetic algorithm, fluctuations of the entropy of a
genome over time are interpreted as fluctuations of the
information that the genome’s organism is storing
about its environment, being this reflected in more
complex organisms. The computation of this entropy
presents technical problems due to the small population
sizes used in practice. In this work we propose and test
an alternative way of measuring the entropy variation
in a population by means of algorithmic information
theory, where the entropy variation between two
generational steps is the Kolmogorov complexity of the
first step conditioned to the second one. We also report
experimental differences in entropy evolution between
systems in which sexual reproduction is present or
absent.This work has been partially sponsored by MICINN,
project TIN2008-02081/TIN and by DGUI
CAM/UAM, project CCG08-UAM/TIC-4425
Optimizations of Patch Antenna Arrays Using Genetic Algorithms Supported by the Multilevel Fast Multipole Algorithm
We present optimizations of patch antenna arrays using genetic algorithms and highly accurate full-wave solutions of the corresponding radiation problems with the multilevel fast multipole algorithm (MLFMA). Arrays of finite extent are analyzed by using MLFMA, which accounts for all mutual couplings between array elements efficiently and accurately. Using the superposition principle, the number of solutions required for the optimization of an array is reduced to the number of array elements, without resorting to any periodicity and similarity assumptions. Based on numerical experiments, genetic optimizations are improved by considering alternative mutation, crossover, and elitism mechanisms. We show that the developed optimization environment based on genetic algorithms and MLFMA provides efficient and effective optimizations of antenna excitations, which cannot be obtained with array-factor approaches, even for relatively simple arrays with identical elements
Natural Selection at Work: An Accelerated Evolutionary Computing Approach to Predictive Model Selection
We implement genetic algorithm based predictive model building as an alternative to the traditional stepwise regression. We then employ the Information Complexity Measure (ICOMP) as a measure of model fitness instead of the commonly used measure of R-square. Furthermore, we propose some modifications to the genetic algorithm to increase the overall efficiency
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