4,065 research outputs found
SQG-Differential Evolution for difficult optimization problems under a tight function evaluation budget
In the context of industrial engineering, it is important to integrate
efficient computational optimization methods in the product development
process. Some of the most challenging simulation-based engineering design
optimization problems are characterized by: a large number of design variables,
the absence of analytical gradients, highly non-linear objectives and a limited
function evaluation budget. Although a huge variety of different optimization
algorithms is available, the development and selection of efficient algorithms
for problems with these industrial relevant characteristics, remains a
challenge. In this communication, a hybrid variant of Differential Evolution
(DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG)
methods within the framework of DE, in order to improve optimization efficiency
on problems with the previously mentioned characteristics. The performance of
the resulting derivative-free algorithm is compared with other state-of-the-art
DE variants on 25 commonly used benchmark functions, under tight function
evaluation budget constraints of 1000 evaluations. The experimental results
indicate that the new algorithm performs excellent on the 'difficult' (high
dimensional, multi-modal, inseparable) test functions. The operations used in
the proposed mutation scheme, are computationally inexpensive, and can be
easily implemented in existing differential evolution variants or other
population-based optimization algorithms by a few lines of program code as an
non-invasive optional setting. Besides the applicability of the presented
algorithm by itself, the described concepts can serve as a useful and
interesting addition to the algorithmic operators in the frameworks of
heuristics and evolutionary optimization and computing
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution
Many real-world control and classification tasks involve a large number of
features. When artificial neural networks (ANNs) are used for modeling these
tasks, the network architectures tend to be large. Neuroevolution is an
effective approach for optimizing ANNs; however, there are two bottlenecks that
make their application challenging in case of high-dimensional networks using
direct encoding. First, classic evolutionary algorithms tend not to scale well
for searching large parameter spaces; second, the network evaluation over a
large number of training instances is in general time-consuming. In this work,
we propose an approach called the Limited Evaluation Cooperative
Co-evolutionary Differential Evolution algorithm (LECCDE) to optimize
high-dimensional ANNs.
The proposed method aims to optimize the pre-synaptic weights of each
post-synaptic neuron in different subpopulations using a Cooperative
Co-evolutionary Differential Evolution algorithm, and employs a limited
evaluation scheme where fitness evaluation is performed on a relatively small
number of training instances based on fitness inheritance. We test LECCDE on
three datasets with various sizes, and our results show that cooperative
co-evolution significantly improves the test error comparing to standard
Differential Evolution, while the limited evaluation scheme facilitates a
significant reduction in computing time
Comparing Evolutionary Operators, Search Spaces, and Evolutionary Algorithms in the Construction of Facial Composites
Facial composite construction is one of the most successful applications of interactive evolutionary computation.
In spite of this, previous work in the area of composite construction has not investigated the
algorithm design options in detail. We address this issue with four experiments. In the first experiment a
sorting task is used to identify the 12 most salient dimensions of a 30-dimensional search space. In the second
experiment the performances of two mutation and two recombination operators for interactive genetic
algorithms are compared. In the third experiment three search spaces are compared: a 30-dimensional
search space, a mathematically reduced 12-dimensional search space, and a 12-dimensional search space
formed from the 12 most salient dimensions. Finally, we compare the performances of an interactive
genetic algorithm to interactive differential evolution. Our results show that the facial composite construction
process is remarkably robust to the choice of evolutionary operator(s), the dimensionality of the search
space, and the choice of interactive evolutionary algorithm. We attribute this to the imprecise nature of human
face perception and differences between the participants in how they interact with the algorithms.
Povzetek: Kompozitna gradnja obrazov je ena izmed najbolj uspešnih aplikacij interaktivnega evolucijskega
ra?cunanja. Kljub temu pa do zdaj na podro?cju kompozitne gradnje niso bile podrobno raziskane
možnosti snovanja algoritma. To vprašanje smo obravnavali s štirimi poskusi. V prvem je uporabljeno
sortiranje za identifikacijo 12 najbolj izstopajo?cih dimenzij 30-dimenzionalnega preiskovalnega prostora.
V drugem primerjamo u?cinkovitost dveh mutacij in dveh rekombinacijskih operaterjev za interaktivni
genetski algoritem. V tretjem primerjamo tri preiskovalne prostore: 30-dimenzionalni, matemati?cno reducirani
12-dimenzionalni in 12-dimenzionalni prostor sestavljen iz 12 najpomembnejših dimenzij. Na
koncu smo primerjali uspešnost interaktivnega genetskega algoritma z interaktivno diferencialno evolucijo.
Rezultati kažejo, da je proces kompozitne gradnje obrazov izredno robusten glede na izbiro evolucijskega
operatorja(-ev), dimenzionalnost preiskovalnega prostora in izbiro interaktivnega evolucijskega algoritma.
To pripisujemo nenatan?cni naravi percepcije in razlikam med interakcijami uporabnikov z algoritmom
Differential evolution with an evolution path: a DEEP evolutionary algorithm
Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs
Differential Evolution-based 3D Directional Wireless Sensor Network Deployment Optimization
Wireless sensor networks (WSNs) are applied more and more widely in real life. In actual scenarios, 3D directional wireless sensors (DWSs) are constantly employed, thus, research on the real-time deployment optimization problem of 3D directional wireless sensor networks (DWSNs) based on terrain big data has more practical significance. Based on this, we study the deployment optimization problem of DWSNs in the 3D terrain through comprehensive consideration of coverage, lifetime, connectivity of sensor nodes, connectivity of cluster headers and reliability of DWSNs. We propose a modified differential evolution (DE) algorithm by adopting CR-sort and polynomial-based mutation on the basis of the cooperative coevolutionary (CC) framework, and apply it to address deployment problem of 3D DWSNs. In addition, to reduce computation time, we realize implementation of message passing interface (MPI) parallelism. As is revealed by the experimentation results, the modified algorithm proposed in this paper achieves satisfying performance with respect to either optimization results or operation time
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