BBOB-benchmarking the generalized generation gap model with parent centric crossover

Pošík, Petr

doi:10.1145/1570256.1570324

Cited by 9 publications

(14 citation statements)

References 2 publications

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“…However, the premature convergence (success rate) of G3-PCX can be improved by the proposed GLMPA which motivates further research in other real-parameter optimisation problems. The G3-PCX has limitations for multi-modal problems and requires a fairly large population size due to the properties of PCX (Pošík, 2009). The GLMPA can address some of its shortcomings.…”

Section: Discussionmentioning

confidence: 99%

“…The G3-PCX needs fairly large population for small problems (Pošík, 2009). In a study, several short experiments revealed that even for two-dimensional problems, a population size of 90 was needed to find the solution reliably.…”

Section: Generalised Generation Gap With Parent-centric Crossovermentioning

confidence: 99%

“…In a study, several short experiments revealed that even for two-dimensional problems, a population size of 90 was needed to find the solution reliably. The population size of 300 was needed in order to solve a 40-dimensional sphere function (Pošík, 2009).…”

Section: Generalised Generation Gap With Parent-centric Crossovermentioning

confidence: 99%

“…A major limitation of G3-PCX is in multi-modal optimisation problems as shown in Pošík (2009) where a restart scheme also showed to be inappropriate. This problem can be handled if more diversity is given to G3-PCX as PCX has more emphasis for local search.…”

Section: Generalised Generation Gap With Parent-centric Crossovermentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Generalised Generation Gap With Parent-centric Crossovermentioning

confidence: 99%

Section: Generalised Generation Gap With Parent-centric Crossovermentioning

confidence: 99%

Section: Generalised Generation Gap With Parent-centric Crossovermentioning

confidence: 99%

See 2 more Smart Citations

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

Chandra

Rolland

2014

Connection Science

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“…The exact descriptions of the algorithms along with the parameter settings can be found in [8] and [7], respectively. The algorithms differ foremost in the following aspects:…”

Section: Algorithm Presentationmentioning

confidence: 99%

Comparison of cauchy EDA and G3PCX algorithms on the BBOB noiseless testbed

Pošík

2010

Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation

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Estimation-of-distribution algorithm equipped with Cauchy sampling distribution is compared with the generalized generation gap algorithm with parent centric crossover. Both algorithms were already presented at the 2009 black-box optimization benchmarking workshop where they often showed similar performance. This paper compares them in more detail and adds to the understanding of their key features and differences.

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Memetic cooperative coevolution of Elman recurrent neural networks

Chandra

2013

Soft Comput

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BBOB-benchmarking the generalized generation gap model with parent centric crossover

Cited by 9 publications

References 2 publications

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

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

Comparison of cauchy EDA and G3PCX algorithms on the BBOB noiseless testbed

Memetic cooperative coevolution of Elman recurrent neural networks

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