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Brief Review of Techniques Used to Develop Adaptive Evolutionary Algorithms
2017
Open Cybernetics and Systemics Journal
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This paper presents a brief review of techniques used to allow evolutionary algorithms to adapt to optimization problems in dynamic environments, through exploration of the control parameters of genetic ...
A description of some of the most used evolutionary techniques is included, with major emphasis on genetic algorithms and their relationship with the problem of adaptation to the environment. ...
Adaptation schemes have also been explored in the context of distributed genetic algorithms. ...
doi:10.2174/1874110x01711010001
fatcat:7qs6kxudznaqlet4zduvi6z25y
THE INFLUENCE OF ENVIRONMENTAL CONDITIONS OVER ADAPTIVE CROSSOVER BASED ON THE DISTRIBUTION OF CHROMOSOMES IN SEARCH SPACE
2017
Scientific Bulletin of Naval Academy
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The adaptability of genetic algorithms is given by adjustment of capacity of their genetic operators by controlling their operating parameters. ...
The aim is both to increase the performance and to obtain an evolutionary algorithm that does not require data provided by the operator and operation specific for each problem. ...
To achieve an adaptive crossover is necessary to develop a mechanism for establishing automatic of this parameter for each issue separately. ...
doi:10.21279/1454-864x-17-i1-075
fatcat:35xctsknkvaq7o67eg255i5mnm
Self Adaptation of Operator Rates in Evolutionary Algorithms
[chapter]
2004
Lecture Notes in Computer Science
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This work introduces a new evolutionary algorithm that adapts the operator probabilities (rates) while evolves the solution of the problem. Each individual encodes its genetic rates. ...
Due to the adaptation mechanism, an individual can leave or move faster from such narrow optimal solution by trying other genetic operators. ...
rate for a generational and a steady state genetic algorithm. ...
doi:10.1007/978-3-540-24854-5_113
fatcat:bena2nt7bvacbc2zw6g6a5xlnq
Impact of the Material Distribution Formalism on the Efficiency of Evolutionary Methods for Topology Optimization
[chapter]
2010
Recent Advances in Optimization and its Applications in Engineering
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We consider an evolutionary method applied to a topology optimization problem. We compare two material distribution formalisms (static vs. ...
We test those four variants on both theoretical and practical test cases, to show that the Voronoi-based formalism combined with adapted reproduction mechanisms performs better and is less sensitive to ...
Standard mechanisms The reproduction mechanisms involved in genetic algorithms are crossover and mutation. ...
doi:10.1007/978-3-642-12598-0_40
fatcat:kg7qnljapfezbjh3aeu66cuqdq
A Self-adaptive Multipeak Artificial Immune Genetic Algorithm
2016
TELKOMNIKA (Telecommunication Computing Electronics and Control)
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This paper proposes an self-adaptive multi-peak immune genetic algorithm (SMIGA) and this algorithm integrates immunity thought in the biology immune system into the evolutionary process of genetic algorithm ...
Genetic algorithm is a global probability search algorithm developed by simulating the biological natural selection and genetic evolution mechanism and it has excellent global search ability, however, ...
By referring to the immune maturation mechanism of biology immune system and integrating genetic algorithm, this paper has proposed an selfadaptive multi-peak immune genetic algorithm, which significantly ...
doi:10.12928/telkomnika.v14i2.2753
fatcat:xv47qrfeenb4bhrmfdpgp6q57y
Research on Resource Scheduling of Cloud Computing Based on Improved Genetic Algorithm
2020
Journal of Electronic Research and Application
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On the technology of the conventional genetic algorithm, an adaptive transformation operator is designed to improve the crossover and mutation of the genetic algorithm. ...
The two-stage resource scheduling optimization simulation is realized by using the conventional genetic algorithm. ...
Figure 2 . 2 chematic diagram of double-point crossover operator
Figure 3 . 3 Adaptive law curve for genetic algorithm crossover and mutation
max Pc 、 min Pc 、 max Pm 、 min Pm is the upper and lower ...
doi:10.26689/jera.v4i2.1156
fatcat:g7lu3wtogvgajdnxhv2bjzrziy
Genetic based scheduling in grid systems: A survey
2013
2013 International Conference on Computer Communication and Informatics
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Genetic algorithm techniques are used in optimization for grid computing as they get their inspirations from evolutionary idea of natural evolution. ...
The grid scheduling optimization problem is modeled as a population of candidate solutions and the genetic algorithm is applied for getting the fittest candidates. ...
D) Adaptive Genetic Algorithm: In Adaptive Genetic Algorithm (AGA) the probabilities of crossover and mutation vary with the fitness, separated by two aspects. ...
doi:10.1109/iccci.2013.6466140
fatcat:my5lnp43ezegtibiumwd7lgkfa
Ant Colony Optimization with Genetic Operations
2013
Automation Control and Intelligent Systems
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Crossover and mutation operations have been adapted for use with ant generated strings which still have to provide feasible solutions. ...
Extensive simulation tests were made in order to determine influence of genetic operation on algorithm performance. ...
Acknowledgments Thanks to Science Publishing reviewers for valuable feedback and provided comments which increased the paper quality. ...
doi:10.11648/j.acis.20130103.13
fatcat:7xpnozezdza23orwtacztzwgqa
PERFORMANCE ANALYSIS OF LINKAGE LEARNING TECHNIQUES IN GENETIC ALGORITHMS
2013
International Journal of Research in Engineering and Technology
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One variance of Genetic Algorithms is a Linkage Learning Genetic Algorithm (LLGA) enhances the efficiencies of Simple Genetic Algorithm (SGA) while solving NP hard Problems. ...
The Gene Silencing mechanism is used to improve the linkages by preserving the building blocks in an individual from the disruption of recombination processes such as Crossover and Mutation. ...
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INTRODUTION Genetic algorithm is an adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics. ...
doi:10.15623/ijret.2013.0212025
fatcat:nqet6psywzei5kx7je24pdf4jy
The most important aspects and operators of genetic algorithm as a stochastic method for solving optimization problems
Najvažniji aspekti i operatori genetskog algoritma kao stohastičke metode za rešavanje optimizacionih problema
2018
Godisnjak Pedagoskog fakulteta u Vranju
Najvažniji aspekti i operatori genetskog algoritma kao stohastičke metode za rešavanje optimizacionih problema
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This paper describes the most important aspects of the genetic algorithm as one of the stochastic methods for solving various classes of optimization problems. ...
It also describes the basic genetic operators: selection, crossover and mutation, which are serving for a new generation of individuals to achieve optimal or good enough solution of the considered optimization ...
This is the basic mechanism for preventing premature convergence of genetic algorithm to a local extreme. ...
doi:10.5937/gufv1802101b
fatcat:onxavt6nkbco5ahnwkv5y3gnly
An Effective Genetic Algorithm for Outlier Detection
2012
International Journal of Computer Applications
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In this paper we are proposing an algorithm to detect outliers using genetic algorithm. The proposed method was exceptionally accurate in identifying the outliers the datasets that we have tested. ...
Detection of such exceptional data's is an important issue in many fields like fraud detection, Intrusion detection and Medicine . ...
CONCLUSION AND FUTURE WORK In this paper, we proposed an new algorithm for outlier detection using genetic algorithm. ...
doi:10.5120/4614-6836
fatcat:ft7vtxw2pfam7ajexn7ci26v4q
Genetic algorithms: a survey
1994
Computer
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Genetic algorithm search methods are rooted in the mechanisms of evolution and natural genetics. ...
Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. n the last five years, genetic algorithms ...
Significant innovations include the distributed genetic algorithms and parallel genetic algorithms. The rest of this section surveys these developments.
Selection mechanisms and scaling. ...
doi:10.1109/2.294849
fatcat:pwvpvkypcfcihh7rss6bfchkuy
On Some Basic Concepts of Genetic Algorithms as a Meta-Heuristic Method for Solving of Optimization Problems
2011
Journal of Software Engineering and Applications
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It also describes the basic genetic operator selection, crossover and mutation, serving for a new generation of individuals to achieve an optimal or a good enough solution of an optimization problem being ...
The paper describes the most important aspects of a genetic algorithm as a stochastic method for solving various classes of optimization problems. ...
This is the basic mechanism for preventing premature convergence of genetic algorithm to a local extreme. ...
doi:10.4236/jsea.2011.48055
fatcat:7bhvwfj3szaqfc5tncve72uujm
Operator and parameter adaptation in genetic algorithms
1997
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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adaptation into Genetic Algorithms. ...
An early example of this was the "Punctuated Crossover" mechanism [Schaffer and Morishima (1987) ] which added extra bits to the representation to encode for crossover points. ...
doi:10.1007/s005000050009
fatcat:z2dskr2il5gdzkciyrckhewhm4
Adaptive genetic operators based on coevolution with fuzzy behaviors
2001
IEEE Transactions on Evolutionary Computation
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This paper presents a technique for adapting control parameter settings associated with genetic operators. ...
Its principal features are: 1) the adaptation takes place at the individual level by means of fuzzy logic controllers (FLCs) and 2) the fuzzy rule bases used by the FLCs come from a separate genetic algorithm ...
Fogel for their valuable comments which have improved the presentation of the paper. ...
doi:10.1109/4235.918435
fatcat:b7btkhyhpzeadplxn54e35o7km
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