A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and o...
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A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and optimization problems are typicallyvery complex with a huge solution space, large number of variables, and multiple objectives. Heuristic algorithms can solve theseproblems in an acceptable time but are usually limited to some particular problem circumstances. On the other hand, evolutionaryalgorithms(EAs), which are general stochastic algorithms inspired by the natural biological evolution and/or social behavior of species, can theoretically be used to solve any complex optimization problems including those found in SDNs. This paper reviewsfour types of EAs that are widely applied in current SDNs: Genetic algorithms(GAs), Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO), and Simulated Annealing(SA) by discussing their techniques, summarizing their representative applications, and highlighting their issues and future works. To the best of our knowledge, our work is the first that compares the tech-niques and categorizes the applications of these four EAs in SDNs.
This paper focuses on automated procedures to reduce the dimensionality of protein structure prediction datasets by simplifying the way in which the primary sequence of a protein is represented. The potential benefits...
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ISBN:
(纸本)9781595936974
This paper focuses on automated procedures to reduce the dimensionality of protein structure prediction datasets by simplifying the way in which the primary sequence of a protein is represented. The potential benefits of this procedure are faster and easier learning process as well as the generation of more compact and human-readable classifiers. The dimensionality reduction procedure we propose consists on the reduction of the, 20-letter amino acid (AA) alphabet, which is normally used to specify a protein sequence, into a lower cardinality alphabet. This reduction comes about by a clustering of AA types accordingly to their physical and chemical similarity. Our automated reduction procedure is guided by a fitness function based on the Mutual Information between the AA-based input attributes of the dataset and the protein structure feature that being predicted. To search for the optimal reduction, the Extended Compact Genetic Algorithm (ECGA) was used, and afterwards the results of this process were fed into (and validated by) BioHEL, a genetics-based machine learning technique. BioHEL used the reduced alphabet to induce rules for protein structure prediction features. BioHEL results are compared to two standard machine learning systems. Our results show that it is possible to reduce the size of the alphabet used for prediction from twenty to just three letters resulting in more compact, i.e. interpretable, rules. Also, a protein-wise accuracy performance measure suggests that the loss of accuracy acrued by this substantial alphabet reduction is not statistically significant when compared to the full alphabet.
The evolutionary edit distance between two individuals in a population, i.e., the amount of applications of any genetic operator it would take the evolutionary process to generate one individual starting from the othe...
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ISBN:
(纸本)9781450349390
The evolutionary edit distance between two individuals in a population, i.e., the amount of applications of any genetic operator it would take the evolutionary process to generate one individual starting from the other, seems like a promising estimate for the diversity between said individuals. We introduce genealogical diversity, i.e., estimating two individuals' degree of relatedness by analyzing large, unused parts of their genome, as a computationally efficient method to approximate that measure for diversity.
While single-objective evolutionary algorithms (EAs) parallelization schemes are both well established and easy to implement, this is not the case for Multi-Objective evolutionary algorithms (MOEAs). Nevertheless, the...
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ISBN:
(纸本)3540249834
While single-objective evolutionary algorithms (EAs) parallelization schemes are both well established and easy to implement, this is not the case for Multi-Objective evolutionary algorithms (MOEAs). Nevertheless, the need for parallelizing MOEAs arises in many real-world applications, where fitness evaluations and the optimization process can be very time consuming. In this paper, we test the 'divide and conquer' approach to parallelize MOEAs, aimed at improving the speed of convergence beyond a parallel island MOEA with migration. We also suggest a clustering based parallelization scheme for MOEAs and compare it to several alternative MOEA parallelization schemes on multiple standard multi-objective test functions.
This study proposes to generalize the hybridization of evolutionary algorithm for solving large dimensional continuous global optimization problems. Inspired by various dual hybridizations being used, this paper propo...
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ISBN:
(纸本)9781479939756
This study proposes to generalize the hybridization of evolutionary algorithm for solving large dimensional continuous global optimization problems. Inspired by various dual hybridizations being used, this paper proposes hybrid evolutionary algorithms based on crossing over the FFA, PSO, BAT, ACO and GA algorithms. The main idea of the proposed method is to integrate the aforementioned algorithms by following best solutions of other algorithm using roulette wheel approach. The aim of the proposed hybrid algorithm was to enable problem solving using two or more evolutionary algorithms as is, without modification, besides effectively exploring and exploiting of the problem search space. Simulations for a series of benchmark test functions justify that an adroit hybridization of various evolutionary algorithms could yield a robust and efficient means of solving wide range of global optimization problems than the standalone evolutionary algorithms.
Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate ...
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ISBN:
(纸本)9781450392686
Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate a gradient. In this work, we study results obtained from using quantum-enhanced operators within the selection mechanism of a genetic algorithm. Our approach frames the selection process as a minimization of a binary quadratic model with which we encode fitness and distance between members of a population, and we leverage a quantum annealing system to sample low energy solutions for the selection mechanism. We benchmark these quantum-enhanced algorithms against classical algorithms over various black-box objective functions, including the OneMax function, and functions from the IOHProfiler library for black-box optimization. We observe a performance gain in average number of generations to convergence for the quantum-enhanced elitist selection operator in comparison to classical on the OneMax function. We also find that the quantum-enhanced selection operator with non-elitist selection outperform benchmarks on functions with fitness perturbation from the IOHProfiler library. Additionally, we find that in the case of elitist selection, the quantum-enhanced operators outperform classical benchmarks on functions with varying degrees of dummy variables and neutrality.
Understanding the impact of crossover in evolutionary algorithms is one of the major challenges in the theoretical analysis of these stochastic search algorithms. Recently, it has been shown that crossover provably he...
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ISBN:
(纸本)9783642158438
Understanding the impact of crossover in evolutionary algorithms is one of the major challenges in the theoretical analysis of these stochastic search algorithms. Recently, it has been shown that crossover provably helps to speed up evolutionary algorithms for the classical all-pairs-shortest path (APSP) problem. In this paper, we extend this approach to the NP-hard multi-criteria APSP problem. Based on rigorous runtime analyses, we point out that crossover leads to better worst case bounds than previous known results. This is the first time that rigorous runtime analyses have shown the usefulness of crossover for an NP-hard multi-criteria optimization problem.
A fixed number of brush strokes images are initialized on a canvas, their position, size, rotation, colour, stroke type and drawing index all randomly chosen. These attributes are then modified by stochastic hillClimb...
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ISBN:
(纸本)9783031037894;9783031037887
A fixed number of brush strokes images are initialized on a canvas, their position, size, rotation, colour, stroke type and drawing index all randomly chosen. These attributes are then modified by stochastic hillClimbing, simulated annealing or the plant propagation algorithm, approximating a target image ever closer. Simulated annealing showed the best performance, followed by hill-Climbing;the plant propagation algorithm performed worst. Finally, the distribution of the attributes of the brush strokes shows us that there appears to be a preference for smaller brush strokes, and strokes of the fourth type.
In this paper, we study the design of combinational logic circuits using evolutionary algorithms. In particular, this paper is about fitness assignment methods and recombination operators for speeding up the optimisat...
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ISBN:
(纸本)9783540781523
In this paper, we study the design of combinational logic circuits using evolutionary algorithms. In particular, this paper is about fitness assignment methods and recombination operators for speeding up the optimisation process. We propose a new fitness assignment mechanism called MaxMin method and compare it with the straightforward method used in the literature. The results show significant improvements both in terms of computational time and quality of the solutions. Furthermore, a new cross-over operator called area cross-over has been introduced and compared with other typical operators. This operator is particularly designed for gate matrices where two rectangular logic blocks are exchanged between the individuals. We observe that the MaxMin fitness assignment as well as the area cross-over operator considerably improve the performance of the evolutionary optimisation.
This paper demonstrates how the efficiency of evolutionary algorithms in dynamic environments can be improved by use of life-time adaptation. Our results contradict the hypothesis that there would be a tradeoff betwee...
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ISBN:
(纸本)0780385152
This paper demonstrates how the efficiency of evolutionary algorithms in dynamic environments can be improved by use of life-time adaptation. Our results contradict the hypothesis that there would be a tradeoff between designing and tuning EAs for static and dynamic environments, in which improved efficiency in one type of environment would decrease the efficiency in the other. In contrast, we show that the inclusion of life-time adaptation can result in EAs that outperform traditional EAs in both static and dynamic environments. Since the performance of EAs with life-time adaptation in dynamic environments are currently poorly understood at best, we conduct an extensive evaluation of the performance of these EAs on combinatorial and continuous dynamic global optimization problems with well-defined characteristics. In doing so, we propose improved benchmark dynamic fitness functions for both the combinatorial and continuous domains, which we have termed Random Dynamics NK-landscapes and Structured Moving Peaks Landscapes, respectively.
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