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SGA search dynamics on second order functions
[chapter]
1998
Lecture Notes in Computer Science
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By comparing its search dynamics to that of a simple O(n 2 ) heuristic, we are able to analyze the behavior of the simple genetic algorithm on second order functions, whose optimization is shown to be ...
Useful information about the optimization problem is, among others, provided by statistical physics: lattice gases can be modeled as second order functions. ...
In view of this complexity, one may wonder whether any metric based on rst order functions can grasp the richness of the second order functions and render it using one single value. ...
doi:10.1007/bfb0026602
fatcat:77xex6hhsrbofislwox6dgaz5i
An improved search space resizing method for model identification by standard genetic algorithm
2017
Systems Science & Control Engineering
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This new method is applied and examined on two processes, a third-order transfer function model with and without random disturbance and raw data of excess oxygen. ...
Second, a boundary resizing method derived from the initial search space value. ...
The second sub-process of PTcA method is the search space boundary optimization by resizing the upper and lower search boundary based on Ts p(Initial) . ...
doi:10.1080/21642583.2017.1289130
fatcat:arhfddkyc5e3ncft2nssv5rtte
An improved search space resizing method for model identification by Standard Genetic Algorithm
2015
2015 21st International Conference on Automation and Computing (ICAC)
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This new method is applied and examined on two processes, a third order transfer function model with and without random disturbance and raw data of excess oxygen. ...
Second, a boundary resizing method derived from the initial search space value. ...
Further SGAs execution enhanced an optimal X i exploitation. The flexibilities and effectiveness of T Sp methods is further assessed on 3 rd order transfer function model with 5% disturbance. ...
doi:10.1109/iconac.2015.7313940
dblp:conf/iconac/RajarathinamGYA15
fatcat:aqjwqpztpfgt5mxxkbrddfocj4
Predetermined time constant approximation method for optimising search space boundary by standard genetic algorithm
2015
Proceedings of the 16th International Conference on Computer Systems and Technologies - CompSysTech '15
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This method is demonstrated on parameter identification of higher order models. ...
In this paper, a new predetermined time constant approximation (Ts p ) method for optimising the search space boundaries to improve SGAs convergence is proposed. ...
INTRODUCTION Search space boundary constraint is one of the common phenomena that lead to premature convergence in standard genetic algorithms (SGAs). ...
doi:10.1145/2812428.2812466
dblp:conf/compsystech/RajarathianmGJY15
fatcat:4jegusc7ivgsjhozjeq7ejwf2e
A comparative study of the canonical genetic algorithm and a real‐valued quantum‐inspired evolutionary algorithm
2009
International Journal of Intelligent Computing and Cybernetics
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functions of varying dimensionality in order to examine its scalability within both static and dynamic environments. ...
Design/Methodology/Approach -This study compares the performance of both the QIEA and the canonical genetic algorithm on a series of test benchmark functions. ...
Fig 5 5 Two dimensional visualization of benchmark functions at start time Fig 6 Two dimensional visualization of benchmark functions after 10 seconds
Fig. 7 Global Search from 100 dimensions to 1000 ...
doi:10.1108/17563780910982716
fatcat:u3y4kmcylzg4ne2havsncev5ae
A Novel Mechanism for Efficient the Search Optimization of Genetic Algorithm
2016
International Journal of Computational Intelligence Systems
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This paper proposes a Social Genetic Algorithm (SGA) that includes a transformation function that has ability to improve search efficiency. ...
In this paper, a new function that optimizes gene relationship has been introduced to advance the evolution capability and flexibility of SGA in searching complex and large solution space. ...
SGA performs similar results to TGA on (T 11 and T 14 ) functions. TGA performs better than SGA on (T 15 and T 16 ) functions. ...
doi:10.1080/18756891.2016.1144153
fatcat:5q4ui4k5lnbprpgxeclp4lrcsq
Evolving Dynamic Change and Exchange of Genotype Encoding in Genetic Algorithms for Difficult Optimization Problems
[article]
2008
arXiv
pre-print
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In this paper, serial alternation strategies between two codings are formulated in the framework of dynamic change of genotype encoding in GAs for function optimization. ...
Likewise, a new variant of GAs for difficult optimization problems denoted Split-and-Merge GA (SM-GA) is developed using a parallel implementation of an SGA and evolving a dynamic exchange of individual ...
INTRODUCTION Genetic algorithms (GAs) are search procedures based on principles derived from the dynamics of natural population genetics. ...
arXiv:0803.4241v1
fatcat:g6z74qgawjcjnlq7h7erpmywpm
Evolving dynamic change and exchange of genotype encoding in genetic algorithms for difficult optimization problems
2007
2007 IEEE Congress on Evolutionary Computation
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In this paper, serial alternation strategies between two codings are formulated in the framework of dynamic change of genotype encoding in GAs for function optimization. ...
Likewise, a new variant of GAs for difficult optimization problems denoted Split-and-Merge GA (SM-GA) is developed using a parallel implementation of an SGA and evolving a dynamic exchange of individual ...
INTRODUCTION Genetic algorithms (GAs) are search procedures based on principles derived from the dynamics of natural population genetics. ...
doi:10.1109/cec.2007.4425063
dblp:conf/cec/BercachiCCV07
fatcat:wab44gmvmvctngqc27j7pxkv7y
Two-phase Search (TPS) Method: Nonbiased and High-speed Parameter Search for Dynamic Models of Biochemical Networks
2009
IPSJ Transactions on Bioinformatics
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We demonstrate that the proposed method enables a nonbiased and high-speed parameter search for dynamic models of biochemical networks through its applications to several benchmark functions and to the ...
A typical solution is to systematically analyze the dynamic behaviors in large parameter space by searching all plausible parameter values without any biases. ...
(TPS) The TPS method combines a random search with a search by GAs in order to achieve a high-speed and nonbiased search. ...
doi:10.2197/ipsjtbio.2.2
fatcat:63n3elrol5bfnixnb2bsjq5cf4
Hyper-selection in dynamic environments
2008
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
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Among these approaches, one kind of methods is to adapt genetic operators in order for genetic algorithms to adapt to a new environment. ...
scheme on the performance of genetic algorithms in combination with several other schemes in dynamic environments. ...
Second, the exact effect of increasing the selection pressure on the performance of SGA depends on the base function used for DOPs. ...
doi:10.1109/cec.2008.4631229
dblp:conf/cec/YangT08
fatcat:vwtlkqrcjbgvbmha65q3paoira
Parameters control in GAs for dynamic optimization
2013
International Journal of Computational Intelligence Systems
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PCDO is tested on six problems generated from Generalized Dynamic Benchmark Generator (GDBG). Experimental results demonstrate that PCDO outperforms other GAs on DOPs. ...
Second, a modified enthusiasm selection is used to adjust the selection pressure. Third, a clustering multi non uniform Mutation is utilized to locate an unexplored search space. ...
The diversity dynamics over generation for SGA and PCDO on DOPs is shown in Figure 3 . ...
doi:10.1080/18756891.2013.754172
fatcat:estatoq54nbn3lqm5ohs4dj43e
Performance Evaluation and Population Reduction for a Self Adaptive Hybrid Genetic Algorithm (SAHGA)
[chapter]
2003
Lecture Notes in Computer Science
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It compares the performance of a self-adaptive hybrid genetic algorithm (SAHGA) to a non-adaptive hybrid genetic algorithm (NAHGA) and the simple genetic algorithm (SGA) on eight different test functions ...
This paper examines the effects of local search on hybrid genetic algorithm performance and population sizing. ...
This algorithm also requires one fitness function evaluation per local search iteration. ...
doi:10.1007/3-540-45105-6_104
fatcat:5n4fbj5w7re3zgwcxyhqnbwnoi
Agent Based Evolutionary Dynamic Optimization
[chapter]
2010
Agent-Based Evolutionary Search
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Simulation experiments on a set of dynamic benchmark problems show the proposed AES algorithm can yield a better performance on dynamic optimization problems (DOPs) in comparison with several peer algorithms ...
In order to increase the predefined energy function, individual agent can compete with its neighbors and also can acquire knowledge through cumulative information. ...
Deceptive Function Deceptive functions are a family of functions where there exists low-order BBs that do not combine to form the higher-order BBs. ...
doi:10.1007/978-3-642-13425-8_5
fatcat:vcwzevytjvfjhg676pklrmpx24
Binary ant algorithm
2007
Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07
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Main differences rely on the way this search space is represented and provided to the colony in order to explore/exploit it, while and more important, we enrol in providing strong evaporation to the problem-habitat ...
In order to tackle this subtle compromise, we propose a novel algorithm for optimization in dynamic binary landscapes, stressing the role of negative feedback mechanisms. ...
Dynamic Schaffer's Function The second experiment was also taken from [19] . ...
doi:10.1145/1276958.1276965
dblp:conf/gecco/FernandesRR07
fatcat:j2t2ut4pzvclhdnt3ytad4j7za
PID controller tuning for a multivariable glass furnace process by genetic algorithm
2016
International Journal of Automation and Computing
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The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. ...
An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria ...
SGAs were applied for identification of a higher order transfer function (3rd order) as a realistic model for EO2, and control oriented models for both Tg and EO2 for control optimisation. ...
doi:10.1007/s11633-015-0910-1
fatcat:e5odyxtgjbhntmaiharqiuk4ba
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