Java evolutionary framework based on genetic programming

Karasek, Jan; Bürget, Radim; Dutta, Maitreyee; Singh, Anushikha

doi:10.1109/spin.2014.6777026

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…Such problems are being solved mainly by meta-heuristic algorithms and their combinations. This paper is related to previous research, were the Genetic Programming (GP) algorithm driven by the Context-free Grammar was developed for the logistic warehouse work-flow optimization [1]. The main contribution of this paper is to introduce three new genetic operators, which is the Swap Job Mutation (SJ), the Swap Work-Plan Mutation (SW), and the Split Job Mutation (SP) and compare their performance to already introduced genetic operators, such as Path Mutation (PA) and Job Order Mutation (JO) [2].…”

Section: Introductionmentioning

confidence: 98%

Genetic programming operators for work-flow optimization in logistic distribution centers

Karasek

Bürget

Povoda

et al. 2014

2014 International Conference on Medical Imaging, M-Health and Emerging Communication Systems (MedCom)

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This paper describes a set of new operators for grammar guided genetic programming algorithm used for workflow optimization in logistic distribution centers. The paper is focused on the description of design of five genetic operators, results of particular operators in logistic work-flow optimization process and comparison of performance of particular operators. The main contribution of this paper is to show the automated problem solving techniques in form of genetic operators applied in genetic programming algorithm which is able to optimize a given problem from the area of logistic warehousing.

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Section: Introductionmentioning

confidence: 98%

Genetic programming operators for work-flow optimization in logistic distribution centers

Karasek

Bürget

Povoda

et al. 2014

2014 International Conference on Medical Imaging, M-Health and Emerging Communication Systems (MedCom)

Self Cite

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“…This is radically different from a framework because, in frameworks, we usually do not have access to the code. As Karasek's work states: there are not many tools in optimization field that allows the researchers to implement own code, modify existing code or compare different algorithms [17]. Also, if the code is not available, it is impossible to adapt the algorithm to specific problems.…”

Section: Introductionmentioning

confidence: 99%

A tool for creating parallel swarm algorithms automatically on multi-core computers

Costa¹,

Cortes²

2019

Learn. Nonlin. Mod.

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Meta-heuristics are usually bio-inspired algorithms (based on genes or social behaviors) that are used for solving optimization problems in a variety of fields and applications. The basic principle of a meta-heuristic, such as genetic algorithms, differential evolutions, particle swarm optimization, etc., is to simulate the pressure that the environment applies to individuals resulting in the survival of the best ones. Regardless of which meta-heuristic is being used, the more complex the problem, the more time consuming the algorithm. In this context, parallel computing represents an attractive way of tackling the necessity for computational power. On the other hand, parallel computing introduces new issues that the programmers have to deal with, such as synchronization and the proper exploration of parallel algorithms/models. To avoid these problems, and at the same time, to provide a fast development of parallel swarm algorithms, this work presents a tool for creating parallel code using Parallel Particle Swarm Optimization (PSO) Algorithms in Java. The generator considers three models of parallelism: master-slaves, island and hierarchical. Experiments in the created code showed that a speedup of 5.3 could be reached in the Island model with 2000 iterations using Griewank's function. Moreover, using a cost estimation model (COCOMO) we showed that our tool could save from 4.4 to 14.5 person/month on programming effort.

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“…Many scholars and experts have carried out a theoretical study and practical simulation of a large number of short term load forecasting. The methods used in the prediction include regression models, time series model, and later to the intelligent models, such as neural network [3][4][5], support vector machine [6] and so on. Neural network has strong nonlinear fitting capability, it can be mapped to arbitrary complex nonlinear relationship through training samples, and can also intelligently adapts to arbitrary nonlinear variation in the short term.…”

Section: Introductionmentioning

confidence: 99%

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

Niu¹,

Xu²,

Lu³

et al. 2014

TOEEJ

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Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

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Java evolutionary framework based on genetic programming

Cited by 3 publications

References 10 publications

Genetic programming operators for work-flow optimization in logistic distribution centers

Genetic programming operators for work-flow optimization in logistic distribution centers

A tool for creating parallel swarm algorithms automatically on multi-core computers

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

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