2,137 research outputs found
CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features
In this paper we propose a crossover operator for evolutionary algorithms
with real values that is based on the statistical theory of population
distributions. The operator is based on the theoretical distribution of the
values of the genes of the best individuals in the population. The proposed
operator takes into account the localization and dispersion features of the
best individuals of the population with the objective that these features would
be inherited by the offspring. Our aim is the optimization of the balance
between exploration and exploitation in the search process. In order to test
the efficiency and robustness of this crossover, we have used a set of
functions to be optimized with regard to different criteria, such as,
multimodality, separability, regularity and epistasis. With this set of
functions we can extract conclusions in function of the problem at hand. We
analyze the results using ANOVA and multiple comparison statistical tests. As
an example of how our crossover can be used to solve artificial intelligence
problems, we have applied the proposed model to the problem of obtaining the
weight of each network in a ensemble of neural networks. The results obtained
are above the performance of standard methods
A Taxonomy for the Crossover Operator for Real-Coded Genetic Algorithms: An Experimental Study
The main real-coded genetic algorithm (RCGA) research effort has been spent on developing
efficient crossover operators. This study presents a taxonomy for this operator that groups its
instances in different categories according to the way they generate the genes of the offspring
from the genes of the parents. The empirical study of representative crossovers of all the
categories reveals concrete features that allow the crossover operator to have a positive influence
on RCGA performance. They may be useful to design more effective crossover models
Multiobjective Simulation Optimization Using Enhanced Evolutionary Algorithm Approaches
In today\u27s competitive business environment, a firm\u27s ability to make the correct, critical decisions can be translated into a great competitive advantage. Most of these critical real-world decisions involve the optimization not only of multiple objectives simultaneously, but also conflicting objectives, where improving one objective may degrade the performance of one or more of the other objectives. Traditional approaches for solving multiobjective optimization problems typically try to scalarize the multiple objectives into a single objective. This transforms the original multiple optimization problem formulation into a single objective optimization problem with a single solution. However, the drawbacks to these traditional approaches have motivated researchers and practitioners to seek alternative techniques that yield a set of Pareto optimal solutions rather than only a single solution. The problem becomes much more complicated in stochastic environments when the objectives take on uncertain (or noisy ) values due to random influences within the system being optimized, which is the case in real-world environments. Moreover, in stochastic environments, a solution approach should be sufficiently robust and/or capable of handling the uncertainty of the objective values. This makes the development of effective solution techniques that generate Pareto optimal solutions within these problem environments even more challenging than in their deterministic counterparts. Furthermore, many real-world problems involve complicated, black-box objective functions making a large number of solution evaluations computationally- and/or financially-prohibitive. This is often the case when complex computer simulation models are used to repeatedly evaluate possible solutions in search of the best solution (or set of solutions). Therefore, multiobjective optimization approaches capable of rapidly finding a diverse set of Pareto optimal solutions would be greatly beneficial. This research proposes two new multiobjective evolutionary algorithms (MOEAs), called fast Pareto genetic algorithm (FPGA) and stochastic Pareto genetic algorithm (SPGA), for optimization problems with multiple deterministic objectives and stochastic objectives, respectively. New search operators are introduced and employed to enhance the algorithms\u27 performance in terms of converging fast to the true Pareto optimal frontier while maintaining a diverse set of nondominated solutions along the Pareto optimal front. New concepts of solution dominance are defined for better discrimination among competing solutions in stochastic environments. SPGA uses a solution ranking strategy based on these new concepts. Computational results for a suite of published test problems indicate that both FPGA and SPGA are promising approaches. The results show that both FPGA and SPGA outperform the improved nondominated sorting genetic algorithm (NSGA-II), widely-considered benchmark in the MOEA research community, in terms of fast convergence to the true Pareto optimal frontier and diversity among the solutions along the front. The results also show that FPGA and SPGA require far fewer solution evaluations than NSGA-II, which is crucial in computationally-expensive simulation modeling applications
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Variable grouping in multivariate time series via correlation
The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the âvariable groupingsâ problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping metho
Genetic learning as an explanation of stylized facts of foreign exchange markets
This paper revisits the Kareken-Wallace model of exchange rate formation in a two-country overlapping generations world. Following the seminal paper by Arifovic (Journal of Political Economy, 104, 1996, 510 â 541) we investigate a dynamic version of the model in which agents? decision rules are updated using genetic algorithms. Our main interest is in whether the equilibrium dynamics resulting from this learning process helps to explain the main stylized facts of free-floating exchange rates (unit roots in levels together with fat tails in returns and volatility clustering). Our time series analysis of simulated data indicates that for particular parameterizations, the characteristics of the exchange rate dynamics are, in fact, very similar to those of empirical data. The similarity appears to be quite insensitive with respect to some of the ingredients of the GA algorithm (i.e. utility-based versus rank-based or tournament selection, binary or real coding). However, appearance or not of realistic time series characteristics depends crucially on the mutation probability (which should be low) and the number of agents (not more than about 1000). With a larger population, this collective learning dynamics looses its realistic appearance and instead exhibits regular periodic oscillations of the agents? choice variables. --learning , genetic algorithms , exchange rate dynamics
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Uncertainty modelling in power system state estimation
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.As a special case of the static state estimation problem, the load-flow problem is studied
in this thesis. It is demonstrated that the non-linear load-flow formulation may be solved
by real-coded genetic algorithms. Due to its global optimisation ability, the proposed
method can be useful for off-line studies where multiple solutions are suspected.
This thesis presents two methods for estimating the uncertainty interval in power system
state estimation due to uncertainty in the measurements. The proposed formulations are
based on a parametric approach which takes in account the meter inaccuracies. A nonlinear
and a linear formulation are proposed to estimate the tightest possible upper and
lower bounds on the states. The uncertainty analysis, in power system state estimation, is
also extended to other physical quantities such as the network parameters. The
uncertainty is then assumed to be present in both measurements and network parameters.
To find the tightest possible upper and lower bounds of any state variable, the problem is
solved by a Sequential Quadratic Programming (SQP) technique.
A new robust estimator based on the concept of uncertainty in the measurements is
developed here. This estimator is known as Maximum Constraints Satisfaction (MCS).
Robustness and performance of the proposed estimator is analysed via simulation of
simple regression examples, D.C. and A.C. power system models.Embassy of Kuwai
On Restricting Real-Valued Genotypes in Evolutionary Algorithms
Real-valued genotypes together with the variation operators, mutation and
crossover, constitute some of the fundamental building blocks of Evolutionary
Algorithms. Real-valued genotypes are utilized in a broad range of contexts,
from weights in Artificial Neural Networks to parameters in robot control
systems. Shared between most uses of real-valued genomes is the need for
limiting the range of individual parameters to allowable bounds. In this paper
we will illustrate the challenge of limiting the parameters of real-valued
genomes and analyse the most promising method to properly limit these values.
We utilize both empirical as well as benchmark examples to demonstrate the
utility of the proposed method and through a literature review show how the
insight of this paper could impact other research within the field. The
proposed method requires minimal intervention from Evolutionary Algorithm
practitioners and behaves well under repeated application of variation
operators, leading to better theoretical properties as well as significant
differences in well-known benchmarks
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