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A genetic algorithm for subset sum problem

Rong Long Wang
2004 Neurocomputing  
In this paper, we consider a DNA procedure for solving the subset sum problem in the Adleman-Lipton model.  ...  The subset sum problem is to find subsets in a given number set, meanwhile number sum of the subset is equal to appointed value. It is a classical NP-complete problem in graph theory.  ...  Such as step (1) Conclusion In this paper, we propose a procedure for the subset sum NP-complete problem in the genetic algorithm model.  ... 
doi:10.1016/j.neucom.2003.12.003 fatcat:rhcuyffwhjecdftef5kdwoudpi

On the Applicability of Genetic Algorithms in Subset Sum Problem

Apeksha Oberoi, Jyoti Gupta
2016 International Journal of Computer Applications  
The problem has been solved using a novel method that uses Genetic Algorithms. Genetic algorithms are generally used for optimization problems.  ...  The subset sum problem is one of the most important NP complete problems. Since the problem is not a deterministic one, an artificial intelligence search techniques can help to find the answers.  ...  Subset sum 2. Genetic algorithm 3. NP problems Table 1 : 1 Literature Review S.N o.  ... 
doi:10.5120/ijca2016910765 fatcat:me7qybu22za4blxufxa4fr5u2a

Yeni Bir Metahuristik Yaklaşımla Alt Küme Toplamı Probleminin Çözümü Ve Performans Analizi

Mustafa Furkan KESKENLER, Eyüp Fahri KESKENLER
2020 El-Cezeri: Journal of Science and Engineering  
Subset sum problem was solved with two different metaheuristic approaches in the study.  ...  Performance analyses were measured on the Subset Sum Problem, defined as NP-Complete problem in computer science, with different functions used in these methods.  ...  Acknowledgments This work was supported by TUBITAK 2211-A Program.  ... 
doi:10.31202/ecjse.660382 fatcat:2apiaqnbhbcupln2wpgaes2hvu

Worst-Case Execution Time Test Generation for Solutions of the Knapsack Problem Using a Genetic Algorithm [chapter]

Maxim Buzdalov, Anatoly Shalyto
2014 Communications in Computer and Information Science  
Moreover, a class of tests that are especially hard for one of the algorithms was discovered by the genetic algorithm.  ...  For randomly generated test data, the expected running time of some algorithms for this problem is linear. We present an approach for generation of tests against algorithms for the knapsack problem.  ...  For subset sum tests, p-values are 0.02643 and 0.01595 for random tests and the genetic algorithm correspondingly.  ... 
doi:10.1007/978-3-662-45049-9_1 fatcat:65yma2qsrfd7pjwq4l6nmqwrva

Phase transitions and symmetry breaking in genetic algorithms with crossover

Alex Rogers, Adam Prügel-Bennett, Nicholas R. Jennings
2006 Theoretical Computer Science  
As an example of such a problem, we consider the subset sum problem.  ...  We calculate the details of this phenomenon on a simple instance of the subset sum problem and show that it is a classic phase transition between ordered and disordered populations.  ...  Section 3 introduces the subset sum problem and describes the experimental results for a genetic algorithm attempting to solve this problem.  ... 
doi:10.1016/j.tcs.2006.04.010 fatcat:ste2gxfkjvbtvcj576pg6hya6m

Modified Genetic Algorithms Based Solution To Subset Sum Problem

Harsh Bhasin, Neha Singla
2012 International Journal of Advanced Research in Artificial Intelligence (IJARAI)  
Subset Sum Problem (SSP) is an NP Complete problem which finds its application in diverse fields. The work suggests the solution of above problem with the help of genetic Algorithms (GAs).  ...  The intent is to develop a generic methodology to solve all NP Complete problems via GAs thus exploring their ability to find out the optimal solution from amongst huge set of solutions.  ...  The problem that has been discussed in the following work is subset sum problem. A Genetic Algorithm based solution has been proposed and analyzed.  ... 
doi:10.14569/ijarai.2012.010107 fatcat:hod3syu5xvfrpjpuipme6inu6i

A Genetic Algorithm Approach for the Multidimensional Two-Way Number Partitioning Problem [chapter]

P. C. Pop, O. Matei
2013 Lecture Notes in Computer Science  
This paper addresses the problem of partitioning a set of vectors into two subsets such that the sums per every coordinate should be exactly or approximately equal.  ...  We propose an efficient genetic algorithm based heuristic for solving the multidimensional two-way number partitioning problem.  ...  This work was supported by a grant of the Romanian National Authority for Scientific Research, CNCS -UEFISCDI, project number PN-II-RU-TE-2011-3-0113.  ... 
doi:10.1007/978-3-642-44973-4_10 fatcat:eeylvhbawzfsppyeaa3icmua3m

GENETIC ALGORITHMS FOR PARTITIONING SETS

WILLIAM A. GREENE
2001 International journal on artificial intelligence tools  
We devise a new genetic algorithm, Eager Breeder, for this problem.  ...  We first revisit a problem from the literature, that of partitioning a given set of numbers into subsets such that their sums are as nearly equal as possible.  ...  To solve this problem, we devise a new genetic algorithm for partitioning sets; it follows an aggressive approach for forming a child's genetic material out of that of the parental partitions; we name  ... 
doi:10.1142/s0218213001000490 fatcat:zi2ziftnbnci7k6sfhpfp2dk24

Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems

J Casillas, O Cordón, M.J Del Jesus, F Herrera
2001 Information Sciences  
In this work, we present a genetic feature selection process that can be integrated in a multistage genetic learning method to obtain, in a more ecient way, FRBCSs composed of a set of comprehensible fuzzy  ...  The inductive learning of a fuzzy rule-based classi®cation system (FRBCS) is made dicult by the presence of a large number of features that increases the dimensionality of the problem being solved.  ...  We also use a feature selection algorithm with ®lter nature [39±41] that searches for a variable cardinality feature subset to obtain the chromosome length for our proposal of genetic feature selection  ... 
doi:10.1016/s0020-0255(01)00147-5 fatcat:4uaknd5pjnawxnupyygc4fryf4

Projects Selection In Knapsack Problem By Using Artificial Bee Colony Algorithm

Armaneesa Naaman Hasoon
2018 Tikrit Journal of Pure Science  
This paper introduces artificial bee colony algorithm to select a subset of project and represented by knapsack problem to put the best investment plan which achieve the highest profits within a determined  ...  The result from the proposed algorithm implemented by matlab (8.3) show the ability to find best solution with precisely and rapidity compared to genetic algorithm.  ...  In [5] implement a fast and efficient genetic algorithm to solve 0-1 knapsack problem feasibility and effectively.  ... 
doi:10.25130/tjps.23.2018.039 fatcat:xmirkwyorbdqvhdna5on6p54ra

Wavelet Based Features for Defect Detection in Fabric using Genetic Algorithm

Prajakta A. Jadhav, Prof.M.S Biradar
2014 IOSR Journal of Computer Engineering  
These coefficients can defect main fabric image & indicate defects of fabric textile by optimal subset of these coefficients. For finding a suitable subset Genetic Algorithm is used defect detector.  ...  The Shannon entropy is used as evaluation function in Genetic Algorithm.  ...  The Genetic Algorithm In a genetic algorithm, a population of candidate solutions to an optimization problem is evolved towards better solutions [4] .  ... 
doi:10.9790/0661-1633116120 fatcat:lurltlre5raghhpuajuxm4dv4a

Niching genetic feature selection algorithms applied to the design of fuzzy rule-based classification systems

Jose Joaquin Aguilera, Manuel Chica, Maria Jose del Jesus, Francisco Herrera
2007 IEEE International Fuzzy Systems conference proceedings  
In this work, different proposals of niching genetic algorithms for the feature selection process are analyzed.  ...  Most of the feature selection algorithms provide a set of variables which are adequate for the induction process according to different quality measures.  ...  Besides the search algorithm, in a feature selection algorithm an important component is the evaluation function that provides a measure of the quality for the feature subsets.  ... 
doi:10.1109/fuzzy.2007.4295638 dblp:conf/fuzzIEEE/AguileraCJH07 fatcat:mqp6edltp5dtpd23xmbgs2vccu

A New Inter-island Genetic Operator for Optimization Problems with Block Properties [chapter]

Wojciech Bożejko, Mieczysław Wodecki
2006 Lecture Notes in Computer Science  
Combinatorial optimization problems of scheduling belongs in most cases to the NP-hard class.  ...  In this paper we propose very effective method of construct parallel algorithms based on island model of coevolutionary algorithm.  ...  We have discussed a new approach to optimization problems with block properties based on the new inter-island genetic operator for the parallel asynchronous coevolutionary algorithm.  ... 
doi:10.1007/11785231_36 fatcat:flzhwwjdkbbntlrlbmi4ywbr6a

Solving the subset interconnection design problem using genetic algorithms

Farts N. AbuAli, Roger L. Wainwright, Dale A. Schoenefeld
1996 Proceedings of the 1996 ACM symposium on Applied Computing - SAC '96  
The genetic algorithm (GA) heuristic is used to nd near optimal solutions for an NP-complete variation of the minimum spanning tree problem. Given a set of vertices V , a cost function c:V V ;!  ...  The minimum subset interconnection design problem is to nd a feasible graph with a minimum cost.  ...  The authors would like to thank Referee No. 4 for his thorough reading of the manuscript, which resulted in numerous improvements and clari cations.  ... 
doi:10.1145/331119.331196 dblp:conf/sac/AbualiWS96 fatcat:d7tvnafbtneurmja3unfi2zfva

Feature Selection using Genetic Algorithm for Clustering high Dimensional Data

Kahkashan Kouser, Amrita Priyam
2018 International Journal of Engineering & Technology  
uses genetic algorithm for searching an effective feature subspace in a large feature space.  ...  First a GA-based feature selection algorithm is designed to determine the optimal feature subset; an optimal feature subset is consisting of important features of the entire data set next, a K-means algorithm  ...  In this paper, we proposed a genetic algorithm based feature selection method, which use the searching capability of genetic algorithm to find a suitable feature subspace for clustering high dimensional  ... 
doi:10.14419/ijet.v7i2.11.11001 fatcat:5wgzmisvyrfi5njemiabhprv6e
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