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An Enhanced Fuzzy-Genetic Algorithm to Solve Satisfiability Problems

José Francisco Saray Villamizar, Youakim Badr, Ajith Abraham
2009 2009 11th International Conference on Computer Modelling and Simulation  
Several works have proposed high-performance algorithms and solvers to explore the space of variables and look for satisfying assignments.  ...  Implementation and experimental results demonstrate the enhancement of solving satisfiability problems.  ...  After a random generating of fuzzy assignations they apply fuzzy logical operations to define the fitness function and solve a satisfiable problem of 200 variables.  ... 
doi:10.1109/uksim.2009.106 dblp:conf/uksim/VillamizarBA09 fatcat:vbnls7js65hw3brejwjhxzz5sa

Evolutionary Algorithms for the Satisfiability Problem

Jens Gottlieb, Elena Marchiori, Claudio Rossi
2002 Evolutionary Computation  
Several evolutionary algorithms have been proposed for the satisfiability problem.  ...  An empirical comparison on commonly used benchmarks is presented for the most successful evolutionary algorithms and for WSAT, a prominent local search algorithm for the satisfiability problem.  ...  For the satisfiability problem, this amounts to considering the MAXSAT fitness function of the best chromosome as a result of a run (the satisfiability problem is then called MAXSAT instead of SAT).  ... 
doi:10.1162/106365602317301763 pmid:11911782 fatcat:7utbvyuaxnhsvduv7advydznzm

A Clausal Genetic Representation and its Evolutionary Procedures for Satisfiability Problems [chapter]

Jin-Kao Hao
1995 Artificial Neural Nets and Genetic Algorithms  
This paper presents a clausal genetic representation for the satisfiability problem (SAT).  ...  Various fitness functions for measuring clausal individuals are identified and their relative merits analyzed. Some preliminary resluts are reported.  ...  Acknowledgments The author would like to thank R. Dorne and S. Vautier for the many useful discussions.  ... 
doi:10.1007/978-3-7091-7535-4_76 dblp:conf/icannga/Hao95 fatcat:trnglwqgpbhezeylisd3tdroeu

ERA: An Algorithm for Reducing the Epistasis of SAT Problems [chapter]

Eduardo Rodriguez-Tello, Jose Torres-Jimenez
2003 Lecture Notes in Computer Science  
It is based on two components: a) An Epistasis Reducer Algorithm (ERA) that produces a more suited representation (with lower epistasis) for a Genetic Algorithm (GA) by preprocessing the original SAT problem  ...  ; and b) A Genetic Algorithm that solves the preprocesed instances.  ...  by a fitness function; this fitness function is evaluated for each chromosome and only the best-fitted individuals survive and become parents for the next generation.  ... 
doi:10.1007/3-540-45110-2_4 fatcat:lwdetnszijciflezmdfrfhjpke

An Application of Genetic Algorithm for University Course Timetabling Problem

Sanjay R., Rajan S.
2016 International Journal of Applied Information Systems  
Then, the genetic representation is determined and a fitness function is established according to the constraints.  ...  Timetabling problem is a process of assigning given set of events and resources to the limited space and time under hard constraints which are rigidly enforced and soft constraints which are satisfied  ...  It is always genetic representation dependent. Once the genetic representation and the fitness function are defined, the evolution which is an iterative process would start.  ... 
doi:10.5120/ijais2016451590 fatcat:rqgywpkye5dqzlbs2nc732j3he

Memetic algorithm behavior on timetabling infeasibility

Tri A. Budiono, Kok Wai Wong
2011 TENCON 2011 - 2011 IEEE Region 10 Conference  
Results show the specification of Memetic Algorithm operators affect the range of problem size in which the algorithm behaves properly.  ...  In this work, we present our analysis on the memetic algorithm behavior on university course timetabling problems to produce feasible timetables.  ...  By MA specification, it means the solution representation, fitness function, genetic operators, move operators and local search strategy that are actually implemented in the MA. II.  ... 
doi:10.1109/tencon.2011.6129070 fatcat:esdwvntitrftlahduvjcejyure

Improving the Performance of a Genetic Algorithm Using a Variable-Reordering Algorithm [chapter]

Eduardo Rodriguez-Tello, Jose Torres-Jimenez
2004 Lecture Notes in Computer Science  
In these cases the choice of the internal representation and genetic operators greatly conditions the result. In this paper a GA and a reordering algorithm were used for solve SAT instances.  ...  These experiments also allow us to observe the relation among the internal representation, the genetic operators and the performance of a GA.  ...  These techniques include adaptive fitness functions, problem-specific genetic operators, and local optimization [8, 9, 24, 13] .  ... 
doi:10.1007/978-3-540-24855-2_10 fatcat:m2pfpkb5yvdxnk5fsiotsuvjaq

Modeling Design Exploration as Co-Evolution

Mary Lou Maher, Josiah Poon
1996 Computer-Aided Civil and Infrastructure Engineering  
This paper introduces a model for problem-design exploration, and how this model can be implemented using the genetic algorithm (GA) paradigm.  ...  Two approaches to implement a co-evolving GA are presented and discussed in this paper: one in which the fitness function is represented within the genotype, and a second in which the fitness function  ...  This work is supported by the Australian Research Council and an Australian Postgraduate Research Award.  ... 
doi:10.1111/j.1467-8667.1996.tb00323.x fatcat:2ofnn5buaneetlxdwiet3o5kxy

Improved Genetic Algorithm Based Classification

Keshavamurthy B. N, Asad Mohammed Khan, Durga Toshniwal
2012 International Journal of Computer Science and Informatics  
The proposed approach improves the evolutionary technique such as genetic algorithm by improving the fitness function parameters.  ...  Also, in this we compare the genetic algorithm results with Naïve Bayes algorithm results. For the experimental work we have used the nursery data set taken from the UCI Machine Learning Repository.  ...  Also genetic operator refinement for a problem plays a vital role for the convergence of search space to an optimal solution.  ... 
doi:10.47893/ijcsi.2012.1040 fatcat:dmu4f25yovdftd5n56fbu5iadm

A Heuristics Approach for Classroom Scheduling Using Genetic Algorithm Technique

Izah R. Ahmad, Suliadi Sufahani, Maselan Ali, Siti N.A.M. Razali
2018 Journal of Physics, Conference Series  
The fitness function for this timetable problem is the inverse of the number of students with class conflicts.  ...  Evaluate Fitness Fitness function is an objective function of problem. Fitness will give the value that then will specify the solution is the best solution or not.  ... 
doi:10.1088/1742-6596/995/1/012050 fatcat:7kpgz7cdfffgvni2mw4u4fdd5m

Genetically Evolved Solution to Timetable Scheduling Problem

Sandesh Timilsina, Rohit Negi, Yashika Khurana, Jyotsna Seth
2015 International Journal of Computer Applications  
The simultaneous advancement in genetic modeling and data computational capabilities has prompted profound interest of scientists across the globe in the field of timetable scheduling.  ...  The wider usage of timetable scheduling in complex data manipulation and computation has attracted many researchers to put forward their theory regarding the use of genetic algorithms.  ...  Probability of elimination will be more for the individual having larger fitness function value as higher the value of the fitness function lesser the fitness of the individual.  ... 
doi:10.5120/20077-2100 fatcat:or6r5uitevh4jjtseei4xxqno4

A Novel Approach to Solve Multiple Traveling Salesmen Problem by Genetic Algorithm [chapter]

András Király, János Abonyi
2010 Studies in Computational Intelligence  
The aim of this paper is to review how genetic algorithms can be applied to solve these problems and propose a novel, easily interpretable representation based GA.  ...  The extension of classical GA tools for mTSP is not a trivial problem, it requires special, interpretable encoding to ensure efficiency.  ...  Acknowledgments The financial support from the TAMOP-4.2.2-08/1/2008-0018 (Élhetőbb környezet, egészségesebb ember -Bioinnováció és zöldtechnológiák kutatása a Pannon Egyetemen, MK/2) project is gratefully  ... 
doi:10.1007/978-3-642-15220-7_12 fatcat:xrpcx65eazbtdho6svkde6hrym

Genetic Folding: Analyzing The Mercer-S Kernels Effect In Support Vector Machine Using Genetic Folding

Mohd A. Mezher, Maysam F. Abbod
2011 Zenodo  
The target of this paper is to answer the question of evolving Mercer-s rule in SVM addressed using either genetic folding satisfied kernel-s rules or not applied to complicated domains and problems.  ...  Genetic Folding (GF) a new class of EA named as is introduced for the first time. It is based on chromosomes composed of floating genes structurally organized in a parent form and separated by dots.  ...  However, SVM has a risk in selecting optimum kernel function to be fitted to the problem in hand. III.  ... 
doi:10.5281/zenodo.1061735 fatcat:jxtvfoylhzfprfztp5deksrsru

A constrained-syntax genetic programming system for discovering classification rules: application to medical data sets

Celia C. Bojarczuk, Heitor S. Lopes, Alex A. Freitas, Edson L. Michalkiewicz
2004 Artificial Intelligence in Medicine  
This paper proposes a new constrained-syntax genetic programming (GP) algorithm for discovering classification rules in medical data sets.  ...  The proposed GP contains several syntactic constraints to be enforced by the system using a disjunctive normal form representation, so that individuals represent valid rule sets that are easy to interpret  ...  The design of the system involved the following aspects: individual representation, genetic operators, fitness function, and classification of new instances.  ... 
doi:10.1016/j.artmed.2003.06.001 pmid:14684263 fatcat:lw3nei7rxzhgxfij5pd5jiboy4

Bicriteria Knapsack Problem with GUB Structure by Hybrid Genetic Algorithm

Masato SASAKI, Mitsuo GEN
2000 Journal of Japan Society for Fuzzy Theory and Systems  
optimizatien problem is introduced for a nun]erica] example.  ...  Then we introduce the following evaluation function for combining the bicriteria objective functions into one overall fitness function and evaluate each chromosome.  ...  Step 5 i Gain the evaluation function value. Step 6 i Genetic operations I Operate the following three genetic operations. Step 3) and improvement algorithm <Step 7).  ... 
doi:10.3156/jfuzzy.12.4_73 fatcat:ple7weobizbadlamdvzqyuwm2u
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