Evolutionary Optimization Framework Research Articles

La Nina: framework in self-adaptive publish/subscribe middleware for wireless sensor networks

This paper focuses on three key issues that the publish/subscribe (pub/sub) communication scheme faces for event routing in wireless sensor networks (WSNs): (1) balancing trade-offs among conflicting performance objectives such as data yield, data fidelity and power efficiency; (2) satisfying quality of service (QoS) requirements such as latency and (3) considering noise in evaluating event routing performance. To address these issues, this paper investigates self-adaptive event routing in TinyDDS, which is pub/sub middleware for WSNs. With its noise-aware and constraint-based evolutionary multiobjective optimisation framework, La Nina, TinyDDS autonomously adapts its routing parameters to dynamic network conditions by reducing the impacts of noise on performance evaluation and seeking the optimal trade-offs among performance objectives under given QoS requirements. Simulation results validate this ability of TinyDDS in large-scale, dynamic and noisy WSNs. TinyDDS is implemented lightweight enough to operate on resource-limited sensor nodes.

Optimization aspects of carcinogenesis

Any process in which competing solutions replicate with errors and numbers of their copies depend on their respective fitnesses is the evolutionary optimization process. As during carcinogenesis mutated genomes replicate according to their respective qualities, carcinogenesis obviously qualifies as the evolutionary optimization process and conforms to common mathematical basis. The optimization view accents statistical nature of carcinogenesis proposing that during it the crucial role is actually played by the allocation of trials. Optimal allocation of trials requires reliable schemas’ fitnesses estimations which necessitate appropriate, fitness landscape dependent, statistics of population. In the spirit of the applied conceptual framework, features which are known to decrease efficiency of any evolutionary optimization procedure (or inhibit it completely) are anticipated as “therapies” and reviewed. Strict adherence to the evolutionary optimization framework leads us to some counterintuitive implications which are, however, in agreement with recent experimental findings, such as sometimes observed more aggressive and malignant growth of therapy surviving cancer cells.

Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization

In this paper, we present a novel surrogate-assisted evolutionary optimization framework for solving computationally expensive problems. The proposed framework uses computationally cheap hierarchical surrogate models constructed through online learning to replace the exact computationally expensive objective functions during evolutionary search. At the first level, the framework employs a data-parallel Gaussian process based global surrogate model to filter the evolutionary algorithm (EA) population of promising individuals. Subsequently, these potential individuals undergo a memetic search in the form of Lamarckian learning at the second level. The Lamarckian evolution involves a trust-region enabled gradient-based search strategy that employs radial basis function local surrogate models to accelerate convergence. Numerical results are presented on a series of benchmark test functions and on an aerodynamic shape design problem. The results obtained suggest that the proposed optimization framework converges to good designs on a limited computational budget. Furthermore, it is shown that the new algorithm gives significant savings in computational cost when compared to the traditional evolutionary algorithm and other surrogate assisted optimization frameworks

Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning

This paper examines the interpretability-accuracy tradeoff in fuzzy rule-based classifiers using a multiobjective fuzzy genetics-based machine learning (GBML) algorithm. Our GBML algorithm is a hybrid version of Michigan and Pittsburgh approaches, which is implemented in the framework of evolutionary multiobjective optimization (EMO). Each fuzzy rule is represented by its antecedent fuzzy sets as an integer string of fixed length. Each fuzzy rule-based classifier, which is a set of fuzzy rules, is represented as a concatenated integer string of variable length. Our GBML algorithm simultaneously maximizes the accuracy of rule sets and minimizes their complexity. The accuracy is measured by the number of correctly classified training patterns while the complexity is measured by the number of fuzzy rules and/or the total number of antecedent conditions of fuzzy rules. We examine the interpretability-accuracy tradeoff for training patterns through computational experiments on some benchmark data sets. A clear tradeoff structure is visualized for each data set. We also examine the interpretability-accuracy tradeoff for test patterns. Due to the overfitting to training patterns, a clear tradeoff structure is not always obtained in computational experiments for test patterns.

Multi-database exploration of large design spaces in the framework of cascade evolutionary structural sizing optimization

In discrete sizing optimization of truss and frame structures the design variables take values from databases, which are usually populated with a relatively small number of cross-section types and sizes. The aim of this work is to allow the use of large-size databases in discrete structural sizing optimization problems, in order to enrich the set of design variable options and increase the potential of achieving high-quality optimal designs. For this purpose, the concept of coarse database is introduced, according to which smaller-size versions of an appropriately ordered large database can be constructed. This concept is combined with the idea of cascading, which allows a single optimization problem to be tackled with a number of autonomous optimization stages. Under this context, several coarse versions of the same full-size database are formed, in order to utilize a different database in each cascade stage executed with an evolutionary optimization algorithm. The first optimization stages of the resulting multi-database cascade procedure make use of the coarsest database versions available and serve the purpose of basic design space exploration. The last stages exploit finer databases (including the original full-size database) and aim in fine tuning the achieved optimal solution. Based on the reported numerical results, multi-database cascading proves to be an effective tool for the handling of large databases and corresponding extensive design spaces in the framework of discrete structural sizing optimization applications.

A framework for evolutionary optimization with approximate fitness functions

It is not unusual that an approximate model is needed for fitness evaluation in evolutionary computation. In this case, the convergence properties of the evolutionary algorithm are unclear due to the approximation error of the model. In this paper, extensive empirical studies are carried out to investigate the convergence properties of an evolution strategy using an approximate fitness function on two benchmark problems. It is found that incorrect convergence will occur if the approximate model has false optima. To address this problem, individual- and generation-based evolution control are introduced and the resulting effects on the convergence properties are presented. A framework for managing approximate models in generation-based evolution control is proposed. This framework is well suited for parallel evolutionary optimization, which is able to guarantee the correct convergence of the evolutionary algorithm, as well as to reduce the computation cost as much as possible. Control of the evolution and updating of the approximate models are based on the estimated fidelity of the approximate model. Numerical results are presented for three test problems and for an aerodynamic design example.

Least-commitment graph matching with genetic algorithms

This paper concerns the correspondence matching of ambiguous feature sets extracted from images. The first contribution made in this paper is to extend Wilson and Hancock's Bayesian matching framework (Wilson and Hancock, IEEE Trans. Pattern Anal. Mach. Intell. 19 (1997) 634–648) by considering the case where the feature measurements are ambiguous. The second contribution is the development of a multimodal evolutionary optimisation framework which is capable of simultaneously producing several good alternative solutions. Previous multimodal genetic algorithms have required additional parameters to be added to a method which is already over-parameterised. The algorithm presented in this paper requires no extra parameters: solution yields are maximised by removing bias in the selection step, while optimisation performance is maintained by a local search step. This framework is in principle applicable to any multimodal optimisation problem where local search performs well. An experimental study demonstrates the effectiveness of the new approach on synthetic and real data.

Rigorous Hitting Times for Binary Mutations

In the binary evolutionary optimization framework, two mutation operators are theoretically investigated. For both the standard mutation, in which all bits are flipped independently with the same probability, and the 1-bit-flip mutation, which flips exactly one bit per bitstring, the statistical distribution of the first hitting times of the target are thoroughly computed (expectation and variance) up to terms of order l (the size of the bitstrings) in two distinct situations: without any selection, or with the deterministic (1 + l)-ES selection on the OneMax problem. In both cases, the 1-bit-flip mutation convergence time is smaller by a constant (in terms of l) multiplicative factor. These results extend to the case of multiple independent optimizers.