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Structural Search Spaces and Genetic Operators

Jonathan E. Rowe, Michael D. Vose, Alden H. Wright
2004 Evolutionary Computation  
In this paper, search space groups with more detailed structure are examined.  ...  ., 2002) , aspects of the theory of genetic algorithms were generalised to the case where the search space, Ω, had an arbitrary group action defined on it.  ...  Structural genetic operators Search spaces We generalize (Vose, 1999) by introducing a class of genetic operators associated with a certain subgroup structure.  ... 
doi:10.1162/1063656043138941 pmid:15768525 fatcat:cubuuumyaremvkv7lqcks6tyoe

Orthogonal Genetic Algorithm and its Application in Traveling Salesman Problem

Han Min Liu, Qing Hua Wu, Xue Song Yan
2012 Advanced Engineering Forum  
orthogonal local search to prevent local convergence to form a new orthogonal genetic algorithm.  ...  Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal test then applied it to the population initialization, crossover operator, as well as the introduction of adaptive  ...  Adaptive local search operator: Local search operator has a strong local search ability, and then can solve the shortcomings of genetic algorithm has the weak ability for the local search.  ... 
doi:10.4028/www.scientific.net/aef.6-7.290 fatcat:tjjaposg25czfazenornci3ole

Genetic Evolution Approach for Target Movement Prediction [chapter]

Sung Baik, Jerzy Bala, Ali Hadjarian, Peter Pachowicz
2004 Lecture Notes in Computer Science  
3D visualization space.  ...  The approach is implemented into the GEM (Genetic Evolution of Movement) system and its performance has been experimentally evaluated.  ...  In order to search other points in the search space, some variation is introduced into the new population by means of idealized "genetic recombination operators."  ... 
doi:10.1007/978-3-540-24687-9_101 fatcat:7ncbckweg5fx7drxzb3vghqcd4

An Interpolation/Extrapolation Process For Creative Designing [chapter]

John S. Gero, Vladimir Kazakov
1999 Computers in Building  
This modification is based on the re-interpretation of the crossover operation of genetic algorithms as an interpolation and its generalization to extrapolation.  ...  This paper introduces a new computational operation that provides support for creative designing by adaptively exploring design state spaces.  ...  The case when both genetic and structure spaces are homomorphic and genetic crossover can be easily expressed in structure space terms.  ... 
doi:10.1007/978-1-4615-5047-1_17 fatcat:mll3l4eznnhy5mfyy74miauywu

Topological Interpretation of Crossover [chapter]

Alberto Moraglio, Riccardo Poli
2004 Lecture Notes in Computer Science  
Building around this definition, a geometric/topological framework for evolutionary algorithms is introduced that clarifies the connection between representation, genetic operators, neighbourhood structure  ...  and distance in the landscape.  ...  Fitness landscapes and genetic operators are undoubtedly connected. Mutation is intuitively associated with the neighbourhood structure of the search space.  ... 
doi:10.1007/978-3-540-24854-5_131 fatcat:r5r6n7s3nvcxbkqoetil32377y

Geometric nelder-mead algorithm on the space of genetic programs

Alberto Moraglio, Sara Silva
2011 Proceedings of the 13th annual conference on Genetic and evolutionary computation - GECCO '11  
Furthermore, PSO and DE have already been derived for the space of genetic programs.  ...  In this paper, we continue this line of research and derive formally a specific NMA for the space of genetic programs.  ...  GNMA SEARCH OPERATORS FOR GENETIC PROGRAMS In order to specify the GNMA to the specific space of genetic programs, we need to choose a distance between genetic programs.  ... 
doi:10.1145/2001576.2001753 dblp:conf/gecco/MoraglioS11 fatcat:txdhfsmzhjbw7bkfrm4olvydq4

Learning with genetic algorithms: An overview

Kenneth De Jong
1988 Machine Learning  
There is now considerable evidence that genetic algorithms are usefifl for global flmction optimization and NP-hard problems.  ...  Genetic algorithms represent a class of adaptive search techniques that have been intensively studied in recent years.  ...  Acknowledgements I would like to thank the editors of this issue~ David Goldberg and John Holland, for useful comments on an earlier draft of the paper.  ... 
doi:10.1007/bf00113894 fatcat:x7bbpxf5hzf5nipasiexlx7tta

Genetic algorithms with local search optimization for protein structure prediction problem

Igor Berenboym, Mireille Avigal
2008 Proceedings of the 10th annual conference on Genetic and evolutionary computation - GECCO '08  
Our algorithm evolves a new local-search genetic operation (called Pull-Move and well described in [2]), into the standard GA 1 ([3,4]).  ...  This paper presents a new Genetic Algorithm for Protein Structure Prediction problem in both 2D and 3D hydrophobichydrophilic lattice models, introduced in [1].  ...  GA Abbreviations used: GA-Genetic Algorithm, HP-Hydrophobic-Hydrophilic, FE/GE-Free/Global Energy, PM-Pull Move, PMGA-Pull Moves with Genetic Algorithm, PSP-Protein Structure Prediction.  ... 
doi:10.1145/1389095.1389296 dblp:conf/gecco/BerenboymA08 fatcat:4ejcrqlpxbgh5o3dsp5d3wdkze

An Improved Robot Path Planning Algorithm

Xuesong Yan Xuesong Yan, Qinghua Wu Qinghua Wu, Hammin Liu
2012 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
well as the introduction of adaptive local search operator to prevent trapped into the local minimum and improvethe convergence speed to form a new genetic algorithm.  ...  Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal design method then applied it to the population initialization, using the intergenerational elite mechanism, as  ...  Adaptive Local Search Operator Local search operator has a strong local search ability, and then can solve the shortcomings of genetic algorithm has the weak ability for the local search.  ... 
doi:10.12928/telkomnika.v10i4.850 fatcat:yidr7dg34zgr7jqinpa6luhpki

An Improved Robot Path Planning Algorithm

Xuesong Yan
2012 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
well as the introduction of adaptive local search operator to prevent trapped into the local minimum and improvethe convergence speed to form a new genetic algorithm.  ...  Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal design method then applied it to the population initialization, using the intergenerational elite mechanism, as  ...  Adaptive Local Search Operator Local search operator has a strong local search ability, and then can solve the shortcomings of genetic algorithm has the weak ability for the local search.  ... 
doi:10.12928/telkomnika.v10i4.412 fatcat:kbum6hbsyrcsncc6lujstjgaqi

Page 131 of American Society of Civil Engineers. Collected Journals Vol. 113, Issue 2 [page]

1987 American Society of Civil Engineers. Collected Journals  
A genetic algorithm only requires payoff (objective function value) in- formation for each of the structures it generates and tests.  ...  Later, the fundamental operators of a genetic algorithm will be ex- amined, but first, we point out one final difference between GAs and more typical search techniques.  ... 

A genetic algorithm with modified crossover operator and search area adaptation for the job-shop scheduling problem

Masato Watanabe, Kenichi Ida, Mitsuo Gen
2005 Computers & industrial engineering  
The genetic algorithm with search area adaptation (GSA) has a capacity for adapting to the structure of solution space and controlling the tradeoff balance between global and local searches, even if we  ...  do not adjust the parameters of the genetic algorithm (GA), such as crossover and/or mutation rates.  ...  GSA has capacity for adapting to the structure of solution space and controlling the tradeoff balance between global and local search abilities dynamically.  ... 
doi:10.1016/j.cie.2004.12.008 fatcat:opsihgflhbazjdcvdh6twyqd5q

An Aggressive Genetic Programming Approach for Searching Neural Network Structure Under Computational Constraints [article]

Zhe Li, Xuehan Xiong, Zhou Ren, Ning Zhang, Xiaoyu Wang, Tianbao Yang
2018 arXiv   pre-print
The challenge in designing such programs lies in how to balance between large search space of the network structures and high computational costs.  ...  on the search space.  ...  search space.  ... 
arXiv:1806.00851v1 fatcat:skbwddtvonhkfkihpjhzyyyrk4

Sets of interacting scalarization functions in local search for multi-objective combinatorial optimization problems

Madalina M. Drugan
2013 2013 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM)  
The genetic scalarization functions assume that the scalarization functions have commonalities that can be exploited using genetic like operators.  ...  SLS is faster because it is searching in a single objective search space but the independent scalarization functions do not systematic exploit the structure of the multi-objective search space.  ...  and stochastic DSLS (SDSLS), where the structure of the search space is exploited with genetic like operators.  ... 
doi:10.1109/mcdm.2013.6595442 dblp:conf/cimcdm/Drugan13 fatcat:sk7bz7kfjjguvdxjewtf65r4sa

Prediction of Protein Tertiary Structure using Genetic Algorithm

G. Sindhu, S. Sudha
2011 International Journal of Electronics Signals and Systems  
In this paper, Genetic Algorithm (GA) based optimization is used. This algorithm is adapted to search the protein conformational search space to find the lowest free energy conformation.  ...  Experimental methods are time consuming and high-priced and it is not always feasible to identify the protein structure experimentally.  ...  CONCLUSION AND FUTURE WORK This paper used Genetic Algorithm with MC and HC/IC crossovers to search the protein conformational search space to find the lowest free energy conformation.  ... 
doi:10.47893/ijess.2011.1019 fatcat:vswphsrzdre5bgkuqtzshit45m
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