Harnessing mutational diversity at multiple levels for improving optimization accuracy in G3-PCX

Teo, Jason; Hijazi, H.A.; Omar, Zaturrawiah Ali; Mohamad, Radziah; Hamid, Yunus

doi:10.1109/cec.2007.4425061

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…There is no mutation operator in the original G3-PCX algorithm. Amendments to the original algorithm have been done by proposing a mutation operator (Teo et al 2007) which has shown good performance in multi-modal problems.…”

Section: G3-pcx Evolutionary Algorithmmentioning

confidence: 99%

Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks

2011

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Adaptation during evolution has been an important focus of research in training neural networks. Cooperative coevolution has played a significant role in improving standard evolution of neural networks by organizing the training problem into modules and independently solving them. The number of modules required to represent a neural network is critical to the success of evolution. This paper proposes a framework for the adaptation of the number of modules during evolution. The framework is called adaptive modularity cooperative coevolution. It is used for training recurrent neural networks on grammatical inference problems. The results shows that the proposed approach performs better than its counterparts as the dimensionality of the problem increases.

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Section: G3-pcx Evolutionary Algorithmmentioning

confidence: 99%

Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks

2011

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“…There is no mutation operator in the original G3-PCX algorithm. Amendments to the original algorithm have been done by proposing a mutation operator (Teo, Hijazi, Omar, Mohamad, & Hamid, 2007) which has shown good performance in multi-modal problems. Further amendments have been done in the PCX operator by introducing a female and male differentiation process which determines the male and female individuals chosen from the population and by further using parent selection mechanisms shown in García-Martínez, Lozano, Herrera, Molina, & Sánchez (2008).…”

Section: Generalised Generation Gap With Parent-centric Crossovermentioning

confidence: 99%

Global–local population memetic algorithm for solving the forward kinematics of parallel manipulators

Chandra

Rolland

2014

Connection Science

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Memetic algorithms (MA) are evolutionary computation methods that employ local search to selected individuals of the population. This work presents global-local population MA for solving the forward kinematics of parallel manipulators. A real-coded generation algorithm with features of diversity is used in the global population and an evolutionary algorithm with parent-centric crossover operator which has local search features is used in the local population. The forward kinematics of the 3RPR and 6-6 leg manipulators are examined to test the performance of the proposed method. The results show that the proposed method improves the performance of the real-coded genetic algorithm and can obtain highquality solutions similar to the previous methods for the 6-6 leg manipulator. The accuracy of the solutions and the optimisation time achieved by the methods in this work motivates for real-time implementation of the 3RPR parallel manipulator.

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“…Other works define mutation operators by means of the combination of other existing ones. Some examples of these are Xue et al (2005); Yang et al (2005); Dong et al (2007); Teo et al (2007); Huang et al (2007b); Fu et al (2008); Chen and Lu (2008); Pant et al (2009).…”

Section: Introductionmentioning

confidence: 96%

A mutation operator based on a Pareto ranking for multi-objective evolutionary algorithms

Mateo

Alberto

2011

J Heuristics

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Evolutionary Algorithms, EA's, try to imitate, in some way, the principles of natural evolution and genetics. They evolve a population of potential solutions to the problem using operators such as mutation, crossover and selection. In general, the mutation operator is responsible for the diversity of the population and helps to avoid the problem of premature convergence to local optima (a premature stagnation of the search caused by the lack of population diversity).In this paper we present a new mutation operator in the context of Multi-Objective Evolutionary Algorithms, MOEA's, which makes use of the definition of Pareto optimality and manages the maximal amplitude or maximal step size of the mutation according to the Pareto layer of the individual and also of the iteration number. The behaviour of our mutation operator reveals that the use of variation operators which take into consideration the quality of the solutions, in terms of Pareto dominance or Pareto layers, can help to improve them. The Pareto based mutation operator proposed is compared with four well established and extensively used mutation operators: random mutation, non-uniform mutation, polynomial mutation and Gaussian mutation. The accomplished experiments reveal that our mutation operator performs, in most of the test problems considered, better than the others.

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Harnessing mutational diversity at multiple levels for improving optimization accuracy in G3-PCX

Cited by 5 publications

References 11 publications

Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks

Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks

Global–local population memetic algorithm for solving the forward kinematics of parallel manipulators

A mutation operator based on a Pareto ranking for multi-objective evolutionary algorithms

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