the proceedings contain 10 papers. the topics discussed include: asymptotic convergence rates for averaging strategies;on crossing fitness valleys with majority-vote crossover and estimation-of-distribution algorithms...
ISBN:
(纸本)9781450383523
the proceedings contain 10 papers. the topics discussed include: asymptotic convergence rates for averaging strategies;on crossing fitness valleys with majority-vote crossover and estimation-of-distribution algorithms;focused jump-and-repair constraint handling for fixed-parameter tractable graph problems;automatic adaptation of hypermutation rates for multimodal optimization;self-adjusting offspring population sizes outperform fixed parameters on the cliff function;non-local optimization: imposing structure on optimization problems by relaxation;on the potential of normalized tsp features for automated algorithm selection;do additional optima speed up evolutionary algorithms?;computing diverse sets of high quality tsp tours by EAX-based evolutionary diversity optimization;and the effect of non-symmetric fitness: the analysis of crossover-based algorithms on RealJump functions.
the paper is a comprehensive discussion of the optimization of Hybrid SDNs withthe aid of metaheuristic algorithms. Hybrid SDNs include central and distributed control; it makes them very flexible and scalable. Yet, ...
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ISBN:
(数字)9798331511890
ISBN:
(纸本)9798331511906
the paper is a comprehensive discussion of the optimization of Hybrid SDNs withthe aid of metaheuristic algorithms. Hybrid SDNs include central and distributed control; it makes them very flexible and scalable. Yet, such environments are hard to optimize the resource allocation and traffic management. To address this, we propose a framework that leverages the power of metaheuristic algorithms, such as geneticalgorithms (GAs), Particle Swarm Optimization (PSO), to fine-tune network parameters and improve overall performance. the framework aims to optimize key performance indicators, including network throughput, latency, and resource utilization, while ensuring network stability and resilience. We show through extensive simulations and evaluations that our proposed approach can indeed achieve significant performance gains compared to traditional control mechanisms. the results show the potential of metaheuristic algorithms in optimizing hybrid SDN architectures and pave the way for more efficient and intelligent network management.
the proceedings contain 18 papers. the topics discussed include: why standard particle swarm optimisers elude a theoretical runtime analysis;on the size of weights in randomized search heuristics;single- and multi-obj...
ISBN:
(纸本)9781605584140
the proceedings contain 18 papers. the topics discussed include: why standard particle swarm optimisers elude a theoretical runtime analysis;on the size of weights in randomized search heuristics;single- and multi-objective evolutionary algorithms for graph bisectioning;on the impact of the mutation-selection balance on the runtime of evolutionary algorithms;computing single source shortest paths using single-objective fitness;black-box search by elimination of fitness functions;additive approximations of pareto-optimal sets by evolutionary multi-objective algorithms;analysis of a simple evolutionary algorithm for the multiobjective shortest path problem;a Gaussian random field model of smooth fitness landscapes;cooperative coevolution and univariate estimation of distribution algorithms;and monotonicity versus performance in co-optimization.
this study presents a novel hybrid diagnostic model for early-stage prostate cancer, integrating geneticalgorithms, neuro-fuzzy logic, and mobile agent technology. By optimizing the selection of key genetic markers, ...
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ISBN:
(数字)9798331511890
ISBN:
(纸本)9798331511906
this study presents a novel hybrid diagnostic model for early-stage prostate cancer, integrating geneticalgorithms, neuro-fuzzy logic, and mobile agent technology. By optimizing the selection of key genetic markers, the system achieves a marked improvement in sensitivity and specificity-up to a 12% increase over conventional diagnostic methods-based on extensive simulations and a controlled clinical dataset. the neuro-fuzzy component adaptively merges genetic, clinical, and historical patient data, refining its predictive accuracy through continuous learning. A decentralized computation strategy enhances scalability and lowers device-level computational overhead, supporting widespread implementation in diverse healthcare environments. While initial results underscore its promise, validation through large-scale clinical trials remains a priority. this approach offers a practical, high-accuracy solution that could significantly reduce late-stage prostate cancer diagnoses, particularly in resourcelimited settings, and stands as a step forward in precision oncology diagnostics. the developed approach can be helpful in diagnosis of the relevant healthcare problem.
In this paper we prove that for a variety of practical problems and representations, there is a free lunch for search algorithmsthat specialise in the task of finding functions or programs that solve problems, such a...
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ISBN:
(纸本)9781605584140
In this paper we prove that for a variety of practical problems and representations, there is a free lunch for search algorithmsthat specialise in the task of finding functions or programs that solve problems, such as genetic programming. In other words, not all such algorithms are equally good under all possible performance measures. We focus in particular on the case where the objective is to discover functions that fit sets of data-points - a task that we will call symbolic regression. We show under what conditions there is a free lunch for symbolic regression, highlighting that these are extremely restrictive.
Activity-based demand generation contructs complete all-day activity plans for each member of a population, and derives transportation demand from the fact that consecutive activities at different locations need to be...
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Activity-based demand generation contructs complete all-day activity plans for each member of a population, and derives transportation demand from the fact that consecutive activities at different locations need to be connected by travel. Besides many other advantages, activity-based demand generation also fits well into the paradigm of multi-agent simulation, where each traveler is kept as an individual throughout the whole modeling process. In this paper, we present a new approach to the problem, which uses geneticalgorithms (GA). Our GA keeps, for each member of the population, several instances of possible all-day activity plans in memory. those plans are modified by mutation and crossover, while 'bad' instances are eventually discarded. Any GA needs a fitness function to evaluate the performance of each instance. For all-day activity plans, it makes sense to use a utility function to obtain such fitness. In consequence, a significant part of the paper is spent discussing such a utility function. In addition, the paper shows the performance of the algorithm to a few selected problems, including very busy and rather non-busy days.
In this paper, a new stop criterion is proposed for geneticalgorithms using a response surface fitted on the best individuals. this criterion is tested on a superconducting magnetic energy storage optimization and co...
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In this paper, a new stop criterion is proposed for geneticalgorithms using a response surface fitted on the best individuals. this criterion is tested on a superconducting magnetic energy storage optimization and compared with stop criteria found in the literature that are reviewed and detailed.
geneticalgorithms (GAs) are applied to the design optimization of electromagnetic devices. Techniques used in the practical implementation of the method are discussed in detail. An application example is given highli...
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geneticalgorithms (GAs) are applied to the design optimization of electromagnetic devices. Techniques used in the practical implementation of the method are discussed in detail. An application example is given highlighting the use of the techniques and indicating the success of the method.
With respect to variable selection for linear regression models, this paper proposes a novel boosting learning method based on genetic algorithm. Its main idea is as follows: each training example is first assigned to...
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ISBN:
(纸本)9781479951512
With respect to variable selection for linear regression models, this paper proposes a novel boosting learning method based on genetic algorithm. Its main idea is as follows: each training example is first assigned to a weight and genetic algorithm is adopted as the base learning algorithm of boosting. then, the training set associated with a weight distribution is taken as the input of genetic algorithm to do variable selection. Subsequently, the weight distribution is updated according to the quality of the previous variable selection results. through repeating the above steps for multiple times, the results are then fused via a weighted combination rule. the performance of the proposed method is investigated on several simulated data sets. the experimental results show that boosting can significantly improve the variable selection performance of a genetic algorithm and can accurately identify the relevant variables.
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