Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways

Renders, Jean-Michel; Bersini, Hugues

doi:10.1109/icec.1994.349948

Cited by 126 publications

(71 citation statements)

References 3 publications

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“…Conversely, it is well-established that, in any of the abovementioned forms, the capabilities of the algorithm are intrinsically more suitable to determine local maxima or minima of the selected function, as its convergence tends to be rather premature [7]. Therefore, in addition to its regular stand-alone use, HillClimbing is often coupled with global techniques capable of covering wider regions of the search area, such as Genetic Algorithms [20,21], when analysing cases where the objective function assumes a significantly complex form.…”

Section: Hill-climbingmentioning

confidence: 99%

Application of a Hill-Climbing technique to the formulation of a new cyclic nonlinear elastic constitutive model

Taborda

Zdravković

2012

Computers and Geotechnics

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Section: Hill-climbingmentioning

confidence: 99%

Application of a Hill-Climbing technique to the formulation of a new cyclic nonlinear elastic constitutive model

Taborda

Zdravković

2012

Computers and Geotechnics

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“…(3) (E<T>E)-type, in which traditional optimization operator is directly used as an evolutionary operator. For example, simplex-GA [22] and simplex coding genetic algorithm [5] are hybridizations of real-coded GA and downhill simplex method [13].…”

Section: Ways Of Hybridizationmentioning

confidence: 99%

“…(2) (E+T+E)-type, in which traditional algorithm is embedded in EA to improve the individuals in the current population. For example, GPL [22] is a combination of real-coded GA and Powell's method. (3) (E<T>E)-type, in which traditional optimization operator is directly used as an evolutionary operator.…”

Section: Ways Of Hybridizationmentioning

confidence: 99%

Low dimensional simplex evolution: a new heuristic for global optimization

Luo

2011

J Glob Optim

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This paper presents a new heuristic for global optimization named low dimensional simplex evolution (LDSE). It is a hybrid evolutionary algorithm. It generates new individuals following the Nelder-Mead algorithm and the individuals survive by the rule of natural selection. However, the simplices therein are real-time constructed and low dimensional. The simplex operators are applied selectively and conditionally. Every individual is updated in a framework of try-try-test. The proposed algorithm is very easy to use. Its efficiency has been studied with an extensive testbed of 50 test problems from the reference (J Glob Optim 31:635-672, 2005). Numerical results show that LDSE outperforms an improved version of differential evolution (DE) considerably with respect to the convergence speed and reliability.

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“…(3) (E < T > E)-type, in which the conventional operator is directly used as an evolutionary operator. For example, simplex-GA [18] and simplex coding genetic algorithm [8] are hybridizations of real-coded GA and simplex method.…”

Section: Introductionmentioning

confidence: 99%

“…(2) (E + T + E)-type, in which the conventional algorithm is embedded in EA to improve the current individual in the process of evolution. For example, Genetic Powell Learning (GPL) is a combination of real-coded GA and Powell's method [18]. (3) (E < T > E)-type, in which the conventional operator is directly used as an evolutionary operator.…”

Section: Introductionmentioning

confidence: 99%