22,054 research outputs found
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
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
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
Negatively Correlated Search
Evolutionary Algorithms (EAs) have been shown to be powerful tools for
complex optimization problems, which are ubiquitous in both communication and
big data analytics. This paper presents a new EA, namely Negatively Correlated
Search (NCS), which maintains multiple individual search processes in parallel
and models the search behaviors of individual search processes as probability
distributions. NCS explicitly promotes negatively correlated search behaviors
by encouraging differences among the probability distributions (search
behaviors). By this means, individual search processes share information and
cooperate with each other to search diverse regions of a search space, which
makes NCS a promising method for non-convex optimization. The cooperation
scheme of NCS could also be regarded as a novel diversity preservation scheme
that, different from other existing schemes, directly promotes diversity at the
level of search behaviors rather than merely trying to maintain diversity among
candidate solutions. Empirical studies showed that NCS is competitive to
well-established search methods in the sense that NCS achieved the best overall
performance on 20 multimodal (non-convex) continuous optimization problems. The
advantages of NCS over state-of-the-art approaches are also demonstrated with a
case study on the synthesis of unequally spaced linear antenna arrays
Evolutionary model type selection for global surrogate modeling
Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist ( Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm ( heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type
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