A new hybrid Self Organizing Migrating Genetic Algorithm for function optimization

Deep, Kusum; Dipti, Dipti

doi:10.1109/cec.2007.4424825

Cited by 25 publications

(27 citation statements)

References 8 publications

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“…The method (13) uses population statistics to determine the rate of change of the step size t (i.e. the solutions in P t at time t are used to determine the step size for time t + 1).…”

Section: Ps-based Local Explorationmentioning

confidence: 99%

“…9. Adjust t adaptively using Equation (13) Population size (N ) 10 × n (n is the problem dimension) 2…”

Section: The Rcga-ps-p Algorithmmentioning

confidence: 99%

“…Hybridization has become an important approach in overcoming the shortcomings of GAs. GAs have been hybridized with other optimization methods to improve their search capability, for example, GA with Nelder-Mead simplex method [11,31], GA with particle swarm optimization [29], GA with self-organizing migrating algorithms [13], and GA with quadratic approximation [12]. Hybrid GAs have been used to solve real-life problems and they have recorded impressive improvements, especially in maintaining population diversity throughout the search process.…”

Section: Introductionmentioning

confidence: 99%

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A comparative study of some real-coded genetic algorithms for unconstrained global optimization

Sawyerr

Ali

Adediran

2011

Optimization Methods and Software

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In this paper, a set of new real-coded genetic algorithms (RCGAs) with local and global exploratory search capabilities are proposed. The search capabilities are based on the inclusion of a modified crossover (MC) procedure and a new global exploratory method in RCGA. The global exploratory method is based on vector projection while the MC procedure is based on a limited version of the pattern search method. These modifications are introduced to increase the efficiency and robustness of RCGAs through better local and global exploration of the search region. An experimental study of the new algorithms was carried out using a set of 57 test problems. Statistical analyses and comparisons of the new algorithms with standard real-coded genetic algorithm (SRCGA) and some recent global optimization algorithms were carried out. Results obtained show that the modifications remarkably improve the performance of RCGAs across the test problems.

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“…The method (13) uses population statistics to determine the rate of change of the step size t (i.e. the solutions in P t at time t are used to determine the step size for time t + 1).…”

Section: Ps-based Local Explorationmentioning

confidence: 99%

“…9. Adjust t adaptively using Equation (13) Population size (N ) 10 × n (n is the problem dimension) 2…”

Section: The Rcga-ps-p Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comparative study of some real-coded genetic algorithms for unconstrained global optimization

Sawyerr

Ali

Adediran

2011

Optimization Methods and Software

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show abstract

“…Unlike the first two functions in the test suite, the Trid function has no local minimum, only the global one, which, for the 10-dimensional problem, is −210 at (10,18,24,28,30,30,28,24,18,10). A primary characteristic of this function is strong coupling between the variables, which causes difficulties for genetic algorithms (Deep 2007). Rosenbrock, the fourth function, is highly non-linear and defined as:…”

Section: Test Problemsmentioning

confidence: 99%

Reference sharing: a new collaboration model for cooperative coevolution

Shi

Gao

2017

J Heuristics

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Cooperative coevolutionary algorithms have been a popular and effective learning approach to solve optimization problems through problem decomposition. However, their performance is highly sensitive to the degree of problem separability. Different collaboration mechanisms usually have to be chosen for particular problems. In the paper, we aim to design a collaboration model that can be successfully applied to a wide range of problems. We present a novel collaboration mechanism that offers this type of potential, along with a new sorting strategy for individuals that are assigned multiple fitness values. Furthermore, we demonstrate and analyze our algorithm through comparison studies with other popular cooperative coevolutionary models on a suite of standard function optimization problems.

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“…Recent works on RCGAs can be found in [1,2,7,8,9,19,20]. Despite the advantages of RCGAs in the continuous parameter domain they are still susceptible to the problem of premature convergence, therefore hybridization has been employed by researchers to prevent RCGAs from falling into premature convergence [3,5,6].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking projection-based real coded genetic algorithm on BBOB-2013 noiseless function testbed

Sawyerr

Adediran

Ali

2013

Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation

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In this paper, a real-coded genetic algorithm (RCGA) which incorporates an exploratory search mechanism based on vector projection termed projection-based RCGA (PRCGA) is benchmarked on the noisefree BBOB 2013 testbed. It is an enhanced version of RCGA-P in [22,23]. The projection operator incorporated in PRCGA shows promising exploratory search capability in some problem landscape. PRCGA is equipped with a multiple independent restart mechanism and a stagnation alleviation mechanism. The maximum number of function evaluations (#F Es) for each test run is set to 10 5 times the problem dimension. PRCGA shows encouraging results on several problems in the low and moderate search dimensions. It is able to solve each type of problem with the dimension up to 40 with lower precision but not all the functions to the desired level of accuracy of 10 −8 especially for high conditioning and multi-modal functions within the specified maximum #F Es.

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A new hybrid Self Organizing Migrating Genetic Algorithm for function optimization

Cited by 25 publications

References 8 publications

A comparative study of some real-coded genetic algorithms for unconstrained global optimization

A comparative study of some real-coded genetic algorithms for unconstrained global optimization

Reference sharing: a new collaboration model for cooperative coevolution

Benchmarking projection-based real coded genetic algorithm on BBOB-2013 noiseless function testbed

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