1,225 research outputs found
A theoretical and empirical study on unbiased boundary-extended crossover for real-valued representation
Copyright © 2012 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences Vol. 183 Issue 1 (2012), DOI: 10.1016/j.ins.2011.07.013We present a new crossover operator for real-coded genetic algorithms employing a novel methodology to remove the inherent bias of pre-existing crossover operators. This is done by transforming the topology of the hyper-rectangular real space by gluing opposite boundaries and designing a boundary extension method for making the fitness function smooth at the glued boundary. We show the advantages of the proposed crossover by comparing its performance with those of existing ones on test functions that are commonly used in the literature, and a nonlinear regression on a real-world dataset
Evolutionary algorithm-based analysis of gravitational microlensing lightcurves
A new algorithm developed to perform autonomous fitting of gravitational
microlensing lightcurves is presented. The new algorithm is conceptually
simple, versatile and robust, and parallelises trivially; it combines features
of extant evolutionary algorithms with some novel ones, and fares well on the
problem of fitting binary-lens microlensing lightcurves, as well as on a number
of other difficult optimisation problems. Success rates in excess of 90% are
achieved when fitting synthetic though noisy binary-lens lightcurves, allowing
no more than 20 minutes per fit on a desktop computer; this success rate is
shown to compare very favourably with that of both a conventional (iterated
simplex) algorithm, and a more state-of-the-art, artificial neural
network-based approach. As such, this work provides proof of concept for the
use of an evolutionary algorithm as the basis for real-time, autonomous
modelling of microlensing events. Further work is required to investigate how
the algorithm will fare when faced with more complex and realistic microlensing
modelling problems; it is, however, argued here that the use of parallel
computing platforms, such as inexpensive graphics processing units, should
allow fitting times to be constrained to under an hour, even when dealing with
complicated microlensing models. In any event, it is hoped that this work might
stimulate some interest in evolutionary algorithms, and that the algorithm
described here might prove useful for solving microlensing and/or more general
model-fitting problems.Comment: 14 pages, 3 figures; accepted for publication in MNRA
Non-elitist Evolutionary Multi-objective Optimizers Revisited
Since around 2000, it has been considered that elitist evolutionary
multi-objective optimization algorithms (EMOAs) always outperform non-elitist
EMOAs. This paper revisits the performance of non-elitist EMOAs for
bi-objective continuous optimization when using an unbounded external archive.
This paper examines the performance of EMOAs with two elitist and one
non-elitist environmental selections. The performance of EMOAs is evaluated on
the bi-objective BBOB problem suite provided by the COCO platform. In contrast
to conventional wisdom, results show that non-elitist EMOAs with particular
crossover methods perform significantly well on the bi-objective BBOB problems
with many decision variables when using the unbounded external archive. This
paper also analyzes the properties of the non-elitist selection.Comment: This is an accepted version of a paper published in the proceedings
of GECCO 201
Evolutionary Computation Methods for Fuzzy Decision Making on Load Dispatch Problems
This chapter introduces basic concepts relating to a day-ahead market in a power system. A load dispatch model considers a ramp rate and valve-point-loading effects. An environment/economic load dispatch model is presented to handle uncertainty factors. The model provides theoretical foundations for the research on operations and decision making in the electric power market. To solve load dispatch problems from day-ahead markets in power systems, a hybrid evolutionary computation method with a quasi-simplex technique, a weight point method for multi-objective programming, and a fuzzy-number-ranking-based optimization method for fuzzy multi-objective non-linear programming are developed
Statistical and Computational Tradeoff in Genetic Algorithm-Based Estimation
When a Genetic Algorithm (GA), or a stochastic algorithm in general, is
employed in a statistical problem, the obtained result is affected by both
variability due to sampling, that refers to the fact that only a sample is
observed, and variability due to the stochastic elements of the algorithm. This
topic can be easily set in a framework of statistical and computational
tradeoff question, crucial in recent problems, for which statisticians must
carefully set statistical and computational part of the analysis, taking
account of some resource or time constraints. In the present work we analyze
estimation problems tackled by GAs, for which variability of estimates can be
decomposed in the two sources of variability, considering some constraints in
the form of cost functions, related to both data acquisition and runtime of the
algorithm. Simulation studies will be presented to discuss the statistical and
computational tradeoff question.Comment: 17 pages, 5 figure
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
Accelerating ant colony optimization by using local search
This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2015.Cataloged from PDF version of thesis report.Includes bibliographical references (page 42-45).Optimization is very important fact in terms of taking decision in mathematics, statistics,
computer science and real life problem solving or decision making application. Many different
optimization techniques have been developed for solving such functional problem. In order to
solving various problem computer Science introduce evolutionary optimization algorithm and
their hybrid. In recent years, test functions are using to validate new optimization algorithms and
to compare the performance with other existing algorithm. There are many Single Object
Optimization algorithm proposed earlier. For example: ACO, PSO, ABC. ACO is a popular
optimization technique for solving hard combination mathematical optimization problem. In this
paper, we run ACO upon five benchmark function and modified the parameter of ACO in order
to perform SBX crossover and polynomial mutation. The proposed algorithm SBXACO is tested
upon some benchmark function under both static and dynamic to evaluate performances. We
choose wide range of benchmark function and compare results with existing DE and its hybrid
DEahcSPX from other literature are also presented here.Nabila TabassumMaruful HaqueB. Computer Science and Engineerin
Self-adaptive simulated binary crossover for real-parameter optimization
Simulated binary crossover (SBX) is a real-parameter recombinationoperator which is commonly used in the evolutionary algorithm (EA) literature. The operatorinvolves a parameter which dictates the spread of offspring solutionsvis-a-vis that of the parent solutions. In all applications of SBX sofar, researchers have kept a fixed value throughout a simulation run. In this paper, we suggest a self-adaptive procedure of updating theparameter so as to allow a smooth navigation over the functionlandscape with iteration. Some basic principles of classicaloptimization literature are utilized for this purpose. The resultingEAs are found to produce remarkable and much better results comparedto the original operator having a fixed value of the parameter. Studieson both single and multiple objective optimization problems are madewith success
- …