6,392 research outputs found
A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation
Evolutionary algorithms have been frequently applied to constrained
continuous optimisation problems. We carry out feature based comparisons of
different types of evolutionary algorithms such as evolution strategies,
differential evolution and particle swarm optimisation for constrained
continuous optimisation. In our study, we examine how sets of constraints
influence the difficulty of obtaining close to optimal solutions. Using a
multi-objective approach, we evolve constrained continuous problems having a
set of linear and/or quadratic constraints where the different evolutionary
approaches show a significant difference in performance. Afterwards, we discuss
the features of the constraints that exhibit a difference in performance of the
different evolutionary approaches under consideration.Comment: 16 Pagesm 2 Figure
Increasing pattern recognition accuracy for chemical sensing by evolutionary based drift compensation
Artificial olfaction systems, which mimic human olfaction by using arrays of gas chemical sensors combined with pattern recognition methods, represent a potentially low-cost tool in many areas of industry such as perfumery, food and drink production, clinical diagnosis, health and safety, environmental monitoring and process control. However, successful applications of these systems are still largely limited to specialized laboratories. Sensor drift, i.e., the lack of a sensor's stability over time, still limits real in dustrial setups. This paper presents and discusses an evolutionary based adaptive drift-correction method designed to work with state-of-the-art classification systems. The proposed approach exploits a cutting-edge evolutionary strategy to iteratively tweak the coefficients of a linear transformation which can transparently correct raw sensors' measures thus mitigating the negative effects of the drift. The method learns the optimal correction strategy without the use of models or other hypotheses on the behavior of the physical chemical sensors
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
In many technical fields, single-objective optimization procedures in
continuous domains involve expensive numerical simulations. In this context, an
improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial
super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide
fast convergence speed, high solution accuracy and robust performance over a
wide range of problems. It implements enhancements of the ABC structure and
hybridizations with interpolation strategies. The latter are inspired by the
quadratic trust region approach for local investigation and by an efficient
global optimizer for separable problems. Each modification and their combined
effects are studied with appropriate metrics on a numerical benchmark, which is
also used for comparing AsBeC with some effective ABC variants and other
derivative-free algorithms. In addition, the presented algorithm is validated
on two recent benchmarks adopted for competitions in international conferences.
Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc
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
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