258,677 research outputs found
Efficient genetic algorithms for solving hard constrained optimization problems
This paper studies many Genetic Algorithm strategies
to solve hard-constrained optimization problems. It investigates the role of various genetic operators to avoid premature convergence. In particular, an analysis of niching methods is carried out on a simple function to show advantages and drawbacks of each of them. Comparisons are also performed on an original benchmark based on an electrode shape optimization technique coupled with a charge simulation metho
Analysis of Different Types of Regret in Continuous Noisy Optimization
The performance measure of an algorithm is a crucial part of its analysis.
The performance can be determined by the study on the convergence rate of the
algorithm in question. It is necessary to study some (hopefully convergent)
sequence that will measure how "good" is the approximated optimum compared to
the real optimum. The concept of Regret is widely used in the bandit literature
for assessing the performance of an algorithm. The same concept is also used in
the framework of optimization algorithms, sometimes under other names or
without a specific name. And the numerical evaluation of convergence rate of
noisy algorithms often involves approximations of regrets. We discuss here two
types of approximations of Simple Regret used in practice for the evaluation of
algorithms for noisy optimization. We use specific algorithms of different
nature and the noisy sphere function to show the following results. The
approximation of Simple Regret, termed here Approximate Simple Regret, used in
some optimization testbeds, fails to estimate the Simple Regret convergence
rate. We also discuss a recent new approximation of Simple Regret, that we term
Robust Simple Regret, and show its advantages and disadvantages.Comment: Genetic and Evolutionary Computation Conference 2016, Jul 2016,
Denver, United States. 201
The Genetic Algorithm for Permutations
The genetic algorithm is a bright example of an
evolutionary algorithm which was developed based on the insights from
theoretical findings. This algorithm uses crossover, and it was shown to
asymptotically outperform all mutation-based evolutionary algorithms even on
simple problems like OneMax. Subsequently it was studied on a number of other
problems, but all of these were pseudo-Boolean.
We aim at improving this situation by proposing an adaptation of the
genetic algorithm to permutation-based problems. Such
an adaptation is required, because permutations are noticeably different from
bit strings in some key aspects, such as the number of possible mutations and
their mutual dependence. We also present the first runtime analysis of this
algorithm on a permutation-based problem called Ham whose properties resemble
those of OneMax. On this problem, where the simple mutation-based algorithms
have the running time of for problem size , the
genetic algorithm finds the optimum in fitness
queries. We augment this analysis with experiments, which show that this
algorithm is also fast in practice.Comment: This contribution is a slightly extended version of the paper
accepted to the GECCO 2020 workshop on permutation-based problem
Structural reliability analysis using response surface method with improved genetic algorithm
For the conventional computational methods for structural reliability analysis, the common limitations are long computational time, large number of iteration and low accuracy. Thus, a new novel method for structural reliability analysis has been proposed in this paper based on response surface method incorporated with an improved genetic algorithm. The genetic algorithm is first improved from the conventional genetic algorithm. Then, it is used to produce the response surface and the structural reliability is finally computed using the proposed method. The proposed method can be used to compute structural reliability easily whether the limit state function is explicit or implicit. It has been verified by two practical engineering cases that the algorithm is simple, robust, high accuracy and fast computation
VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.
The Genetic Algorithm is an area in the field of Artificial Intelligence that is
founded on the principles of biological evolution. Visualization techniques help in
understanding the searching behaviour of Genetic Algorithm. lt also makes possible
the user interactions during the searching process. It is noted that active user
intervention increases the acceleration of Genetic Algorithm towards an optimal
solution.
In proposed research work, the user is aided by a visualization based on the
representation of multidimensional Genetic Algorithm data on 2-0 space. The aim of
the proposed approach is to study the benefit of using visualization techniques to
explorer Genetic Algorithm data based on gene values. The user participates in the
search by proposing a new individual. This is difTerent from existing Interactive
Genetic Algorithm in which selection and evaluation of solutions is done by the users.
A tool termed as VIGA-20 (Visualization of Genetic Algorithm using 2-0 Graph) is
implemented to accomplish this goal. This visual tool enables the display of the
evolution of gene values from generation to generation to observing and analysing the
behaviour of the search space with user interactions. Individuals for the next
generation are selected by using the objective function. Hence, a novel humanmachine
interaction is developed in the proposed approach.
The efficiency of the proposed approach is evaluated by two benchmark
functions. The analysis and comparison of VIGA-20 is based on convergence test
against the results obtained from the Simple Genetic Algorithm. This comparison is
based on the same parameters except for the interactions of the user. The application
of proposed approach is the modelling the branching structures by deriving a rule
from best solution of VIGA-20. The comparison of results is based on the different
user's perceptions, their involvement in the VIGA-20 and the difference of the fitness
convergence as compared to Simple Genetic Algorithm
Multiple damage detection and localization in beam-like and complex structures using co-ordinate modal assurance criterion combined with firefly and genetic algorithms
Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm
Nemo: a computational tool for analyzing nematode locomotion
The nematode Caenorhabditis elegans responds to an impressive range of
chemical, mechanical and thermal stimuli and is extensively used to investigate
the molecular mechanisms that mediate chemosensation, mechanotransduction and
thermosensation. The main behavioral output of these responses is manifested as
alterations in animal locomotion. Monitoring and examination of such
alterations requires tools to capture and quantify features of nematode
movement. In this paper, we introduce Nemo (nematode movement), a
computationally efficient and robust two-dimensional object tracking algorithm
for automated detection and analysis of C. elegans locomotion. This algorithm
enables precise measurement and feature extraction of nematode movement
components. In addition, we develop a Graphical User Interface designed to
facilitate processing and interpretation of movement data. While, in this
study, we focus on the simple sinusoidal locomotion of C. elegans, our approach
can be readily adapted to handle complicated locomotory behaviour patterns by
including additional movement characteristics and parameters subject to
quantification. Our software tool offers the capacity to extract, analyze and
measure nematode locomotion features by processing simple video files. By
allowing precise and quantitative assessment of behavioral traits, this tool
will assist the genetic dissection and elucidation of the molecular mechanisms
underlying specific behavioral responses.Comment: 12 pages, 2 figures. accepted by BMC Neuroscience 2007, 8:8
Caregiver Assessment Using Smart Gaming Technology: A Preliminary Approach
As pre-diagnostic technologies are becoming increasingly accessible, using
them to improve the quality of care available to dementia patients and their
caregivers is of increasing interest. Specifically, we aim to develop a tool
for non-invasively assessing task performance in a simple gaming application.
To address this, we have developed Caregiver Assessment using Smart Gaming
Technology (CAST), a mobile application that personalizes a traditional word
scramble game. Its core functionality uses a Fuzzy Inference System (FIS)
optimized via a Genetic Algorithm (GA) to provide customized performance
measures for each user of the system. With CAST, we match the relative level of
difficulty of play using the individual's ability to solve the word scramble
tasks. We provide an analysis of the preliminary results for determining task
difficulty, with respect to our current participant cohort.Comment: 7 pages, 1 figures, 6 table
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