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15
An Indexed Bibliography of Genetic Algorithms in Power Engineering
, 1995
"... s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceed ..."
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Cited by 79 (10 self)
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s: Jan. 1992  Dec. 1994 ffl CTI: Current Technology Index Jan./Feb. 1993  Jan./Feb. 1994 ffl DAI: Dissertation Abstracts International: Vol. 53 No. 1  Vol. 55 No. 4 (1994) ffl EEA: Electrical & Electronics Abstracts: Jan. 1991  Dec. 1994 ffl P: Index to Scientific & Technical Proceedings: Jan. 1986  Feb. 1995 (except Nov. 1994) ffl EI A: The Engineering Index Annual: 1987  1992 ffl EI M: The Engineering Index Monthly: Jan. 1993  Dec. 1994 The following GA researchers have already kindly supplied their complete autobibliographies and/or proofread references to their papers: Dan Adler, Patrick Argos, Jarmo T. Alander, James E. Baker, Wolfgang Banzhaf, Ralf Bruns, I. L. Bukatova, Thomas Back, Yuval Davidor, Dipankar Dasgupta, Marco Dorigo, Bogdan Filipic, Terence C. Fogarty, David B. Fogel, Toshio Fukuda, Hugo de Garis, Robert C. Glen, David E. Goldberg, Martina GorgesSchleuter, Jeffrey Horn, Aristides T. Hatjimihail, Mark J. Jakiela, Richard S. Judson, Akihiko Konaga...
Combining Cellular Genetic Algorithms and Local Search for Solving Satisfiability Problems
 In Proceedings of Tenth IEEE International Conference on Tools with Artificial Intelligence
, 1998
"... A new parallel hybrid method for solving the satisfiability problem that combines cellular genetic algorithms and the random walk (WSAT ) strategy of GSAT is presented. The method, called CGWSAT , uses a cellular genetic algorithm to perform a global search on a random initial population of candidat ..."
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Cited by 10 (0 self)
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A new parallel hybrid method for solving the satisfiability problem that combines cellular genetic algorithms and the random walk (WSAT ) strategy of GSAT is presented. The method, called CGWSAT , uses a cellular genetic algorithm to perform a global search on a random initial population of candidate solutions and a local selective generation of new strings. Global search is specialized in local search by adopting the WSAT strategy. CGWSAT has been implemented on a Meiko CS2 parallel machine using a twodimensional cellular automaton as parallel computation model. The algorithm has been tested on randomly generated problems and some classes of problems from the DIMACS test set. 1. Introduction The satisfiability problem (SAT ) consists in finding a truth assignment that makes a boolean expression true. Satisfiability plays a central role in a broad range of fields such as artificial intelligence, mathematical logic, computer vision, VLSI design, databases, automated reasoning, comp...
GASAT: A genetic local search algorithm for the satisfiability problem
 Evolutionary Computation
"... This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare it ..."
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Cited by 8 (0 self)
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This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare its overall performance with stateoftheart SAT algorithms. These experiments show that GASAT provides very competitive results.
GAeasy and GAhard Constraint Satisfaction Problems
, 1995
"... In this paper we discuss the possibilities of applying genetic algorithms (GA) for solving constraint satisfaction problems (CSP). We point out how the greediness of deterministic classical CSP solving techniques can be counterbalanced by the random mechanisms of GAs. We tested our ideas by running ..."
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Cited by 7 (2 self)
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In this paper we discuss the possibilities of applying genetic algorithms (GA) for solving constraint satisfaction problems (CSP). We point out how the greediness of deterministic classical CSP solving techniques can be counterbalanced by the random mechanisms of GAs. We tested our ideas by running experiments on four different CSPs: Nqueens, graph 3colouring, the traffic lights and the Zebra problem. Three of the problems have proven to be GAeasy, and even for the GAhard one the performance of the GA could be boosted by techniques familiar in classical methods. Thus GAs are promising tools for solving CSPs. In the discussion, we address the issues of nonsolvable CSPs and the generation of all the solutions. 1.1 Introduction In this paper we consider genetic algorithms (GA) for solving constraint satisfaction problems (CSP) with finite domains. The majority of CSP solving algorithms, which we will refer to as classical ones, are deterministic and constructive search algorithms....
A CLAUSAL GENETIC REPRESENTATION AND ITS EVOLUTIONARY PROCEDURES FOR SATISFIABILITY PROBLEMS
, 1995
"... This paper presents a clausal genetic representation for the satisfiability problem (SAT). This representation, CR for short, aims to conserve the intrinsical relations between variables for a given SAT instance. Based on CR, a set of evolutionary algorithms (EAs) are defined. In particular, a class ..."
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This paper presents a clausal genetic representation for the satisfiability problem (SAT). This representation, CR for short, aims to conserve the intrinsical relations between variables for a given SAT instance. Based on CR, a set of evolutionary algorithms (EAs) are defined. In particular, a class of hybrid EAs integrating local search into evolutionary operators are detailed. Various fitness functions for measuring clausal individuals are identified and their relative merits analyzed. Some preliminary resluts are reported.
Local Search Methods
, 2006
"... Local search is one of the fundamental paradigms for solving computationally hard combinatorial problems, including the constraint satisfaction problem (CSP). It provides the basis for some of the most successful and versatile methods for solving the large and difficult problem instances encountered ..."
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Local search is one of the fundamental paradigms for solving computationally hard combinatorial problems, including the constraint satisfaction problem (CSP). It provides the basis for some of the most successful and versatile methods for solving the large and difficult problem instances encountered in many reallife applications. Despite impressive advances in systematic, complete search algorithms, local search methods in many cases represent the only feasible way for solving these large and complex instances. Local search algorithms are also naturally suited for dealing with the optimisation criteria arising in many practical applications. The basic idea underlying local search is to start with a randomly or heuristically generated candidate solution of a given problem instance, which may be infeasible, suboptimal or incomplete, and to iteratively improve this candidate solution by means of typically minor modifications. Different local search methods vary in the way in which improvements are achieved, and in particular, in the way in which situations are handled in which no direct improvement is possible. Most local search methods use randomisation to ensure that the search process does not
A Hybrid Genetic Algorithm for the Satisfiability Problem
"... This paper introduces a hybrid genetic algorithm for the satisfiability problem (SAT). This algorithm, called GASAT, incorporates local search within the genetic framework. GASAT relays on a problem specific crossover operator to create new solutions, that are improved by a tabu search procedure. Th ..."
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Cited by 3 (0 self)
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This paper introduces a hybrid genetic algorithm for the satisfiability problem (SAT). This algorithm, called GASAT, incorporates local search within the genetic framework. GASAT relays on a problem specific crossover operator to create new solutions, that are improved by a tabu search procedure. The performance of GASAT is assessed using a set of wellknown benchmarks. Comparisons with stateoftheart SAT algorithms show that GASAT gives competitive results.
Solving the Satisfiability Problem by a Parallel Cellular Genetic Algorithm
 PROC. OF EUROMICRO WORKSHOP ON COMPUTATIONAL INTELLIGENCE, IEEE COMPUTER
, 1998
"... This paper presents a new evolutionary method for solving the satisfiability problem. It is based on a parallel cellular genetic algorithm which performs global search on a random initial population of individuals and local selective generation of new strings according to new defined genetic operato ..."
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This paper presents a new evolutionary method for solving the satisfiability problem. It is based on a parallel cellular genetic algorithm which performs global search on a random initial population of individuals and local selective generation of new strings according to new defined genetic operators. The algorithm adopts a diffusion model of information among chromosomes by realizing a twodimensional cellular automaton. Global search is then specialized in local search by changing the assignment of a variable that leads to the greatest decrease in the total number of unsatisfied clauses. A parallel implementation of the algorithm has been realized on a CS2 parallel machine.
CHSAT: A Complete Heuristic Procedure for Satisfiability Problems
 In proceedings of the ECAI'96 Workshop on Advances in Propositional Deduction
, 1996
"... . This paper presents CHSAT, a Complete Heuristic procedure for the SATisfiability problem (SAT) based on local search techniques. CHSAT aims to combine the efficiency of local search and the completeness of memorizingbacktracking. CHSAT extends successively a consistent partial assignment using ..."
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. This paper presents CHSAT, a Complete Heuristic procedure for the SATisfiability problem (SAT) based on local search techniques. CHSAT aims to combine the efficiency of local search and the completeness of memorizingbacktracking. CHSAT extends successively a consistent partial assignment using a complete assignment as guidance for variableordering. To extend the partial assignment, CHSAT uses a twostep selection strategy to determine the next variable: the selection of an unsatisfiable clause followed by the selection of a variable in this clause. If the partial assignment can no longer be extended, it is memorized in the form of a new clause before the search restarts. Experiments on Dimacs benchmarks show the interest of CHSAT for solving some classes of hard instances. 1 INTRODUCTION The satisfiability problem (SAT) [5] is of great importance both in theory and in practice. The statement of the problem is very simple. Given a wellformed boolean expression E, is there a t...
SAT Solving Using an Epistasis Reducer Algorithm plus a GA
"... A novel method, for solving satisfiability (SAT) instances is presented. 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 A ..."
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A novel method, for solving satisfiability (SAT) instances is presented. 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 preprocessed instances. ERA is implemented by a simulated annealing algorithm (SA), which transforms the original SAT problem by rearranging the variables to satisfy the condition that the most related ones are in closer positions inside the chromosome. Results of experimentation demonstrated that the proposed combined approach outperforms GA in all the tests accomplished. 1.