Spread-Repair Algorithm for Solving Extended Fuzzy Constraint Satisfaction Problems

Sudo, Yasuhiro; Kurihara, Masahito; Mitamura, Tamotsu

doi:10.1007/3-540-32391-0_95

Cited by 4 publications

(4 citation statements)

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“…The Spread-Repair(SR) algorithm is a FCSP solver developcd by the authors [5]. Although SR is an efficient local search algorithm exploiting the smcmre of FCSPs, we have recently found that there is still some rcom for improving its efficiency In this paper, we extend this local search method by combining it with a systematic tree search scheme to present a revised algorithm, called Spread-RcpairShrink (SRS).…”

Section: Yasuhiro Sudo Is With the Graduate S C H D Ofmentioning

confidence: 98%

“…Then this shrinking process is repeated recursively until the focus reaches the root or it turns out that the focus cannot be repaired any more. Note that SRS performs efficiently by maintaining the path from c* to the rcpaired node in the search tree, while our prcvious algorithm SR [5] has to rcstart the spreading from thc scratch alter each repair or the target.…”

Section: Maximize Min (Pand ( V L [ S I ] ) ) 00 I N Mmentioning

confidence: 99%

“…In this section, we compare the q~lality of sol~~tions of SRS to with the strictly optimal solutions obtained by Forward-Checking (FC) [6], which is a kind of systematic methods based on the hranch-and-bound algorithm well-known for its good performance [3]. As for the CPU time, SRS is compared with Fuzzy GENET pGENET) [8] based on ncural nctworks and with our previous algorithm Spread-R@ (SR) [5] obtained from SRS by removing the Slnink operation.…”

Section: ) Srs Algorithmmentioning

confidence: 99%

“…I) : The Basic Structure: As discussed in the previous section, the aim of FCSPs is to maximize the degree of satisfaction of the most violated constraints (c*'s). To solve the problems based on the local search framework, the Spread-Repair (SR) algorithm developed by the authors in [5] tries to repair a target constraint c* repetitively by changing the value of a variable in its scope. When trapped in a local maximum, SR tries to escape from the state by the operation called spreading, which changes the target of the repair to the 'neighbor' constraints surrounding the current target.…”

Section: B Srs Algorithmmentioning

confidence: 99%

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Spread-Repair-Shrink: A Hybrid Algorithm for Solving Fuzzy Constraint Satisfaction Problems

Sudo

Kurihara

2006

2006 IEEE International Conference on Fuzzy Systems

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Abslract-A fmzy constraint satisfaction problem (FCSP) isThe other is local state-spa= search algorithms based on itan extension of the clar*ical CSP, a po~erful tool for modeling erative improvement in incons~stent, full assignments. These problems On co-ints among techniques have been developed h o s t independently, but d y , the algorithms for solving CSPs are c M e d into two categories: the mtemtic search (eomnlete methods based on recently, much attention has k n paid to hybrid methods in-~~~~~ -~ ~ ~"~~~ ~~ ~ ~~~~ ~ ~~~ A ~~~ ~~ ~~~ ~~~~~~ ~ WITh trees) and the 1-1 search methods tegrating the virtues of cach class of algorithms. An example on iterative imurovement). Both haie meritr and demerits. is the decision-renair algorithm develoned in 111. an extended ->.Recently, muchittention i;as been paid to hybrid methods for version of a pr-W-g paper pre&ntd at A~AI.2000. integatin~ both merits to solve CSPs efficientl3: but almost no FroIn the viewpoint of soft computing, however, the naattempt has been made so far for solving FCSPs. I. tlk paper, we prsent a hybrid, approximate method ditional CSP is too rigid to formulate real world problems. for xsps. ~h~ method, eaued the ~~~d -~~~~i , -.In order to improve this situation, much work has been done Shrink (SRS) algorithm. combines a svstematic WIT^ with the on the extensions of CSPs. The fuzzv CSP (FCSP) is one of Spr-d-~epair 6~) Qorithl~ a lo&WIT11 method recently such extensions, where constraints are represented by fuzzy developed by the authors SRS repeats spreading and * U relations, which admit incomplete solutiotlS aroviding a set of search trees in order to repair local constraints until the satbfaction decree of the worst constraints are useful information Tor solving real-world problems[2], [3], the roots of the t&) is improved. We empiricalli show that SRS out~erfonns SR and other well-known methods such as ~orn,anl'~'hrckill~ and Fuzq (;T.NET. a hen wr nant to qairkly ct.1 a xwdqualih' uppruuin~ale wlutiun uf suflicit.ntl\. lan~t. siltA constraini is a restriction on a space of possibilities; it is a piece of knowledge that narrows the scope of this space. Because constraints aise naturally in m t areas of h u m endeavor, they are the most general mcans for formulating regularities that govern our computational, physical, biological, and social worlds. Many problems arising in such domains can be naturally modeled as constraint satisfaction problem (CSPs). A CSP consists of a finite set of variables, each associated with a h i t c domain of values, and a set of const~aints among the variables. A solution is an assignment of a value to every variable such that all constraints are satisfied Since CSPs are NP-hard problems in general. no efficient and complete algorithms for solving CSPs exist and the increase in the worst-case computation time is exponential in the size of the problems. In most of the cases, however, we can obtain a solution in practical time by using incomplctc algorithms.CSP algorithms are usually categorized into two classes...

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Section: Yasuhiro Sudo Is With the Graduate S C H D Ofmentioning

confidence: 98%

Section: Maximize Min (Pand ( V L [ S I ] ) ) 00 I N Mmentioning

confidence: 99%