### Abstract

Local search (LS) methods heuristically find a solution for constraint satisfaction problems (CSP). LS starts the search for a solution from a random assignment. LS then examines the neighbours of this assignment to determine a better neighbour valuation to move to. It repeats this process of moving from the current assignment to a better assignment until it finds a solution that satisfies all constraints. ICM considers some of the constraints as hard constraints that are always satisfied. In this way, ICM reduces the possible neighbours in each move and hence the overall search space. ICM chooses the hard constraints in such away that the space of valuations that satisfies these constraints is connected in order to guarantee that a local search can reach any solution from any valuation in this space. In this paper, we incorporate ICM into one of the most recent local search algorithm, ESG, and we show the improvement of the new algorithm. We ran this algorithm in different SAT problems.

Original language | English |
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Title of host publication | Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence |

Editors | I. Russell, Z. Markov |

Pages | 270-275 |

Number of pages | 6 |

Publication status | Published - 2005 |

Event | Recent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Clearwater Beach, FL Duration: 15 May 2005 → 17 May 2005 |

### Other

Other | Recent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 |
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City | Clearwater Beach, FL |

Period | 15/5/05 → 17/5/05 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence*(pp. 270-275)

**Speeding up the ESG algorithm.** / Kilani, Yousef; Mohd. Zin, Abdullah.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence.*pp. 270-275, Recent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005, Clearwater Beach, FL, 15/5/05.

}

TY - GEN

T1 - Speeding up the ESG algorithm

AU - Kilani, Yousef

AU - Mohd. Zin, Abdullah

PY - 2005

Y1 - 2005

N2 - Local search (LS) methods heuristically find a solution for constraint satisfaction problems (CSP). LS starts the search for a solution from a random assignment. LS then examines the neighbours of this assignment to determine a better neighbour valuation to move to. It repeats this process of moving from the current assignment to a better assignment until it finds a solution that satisfies all constraints. ICM considers some of the constraints as hard constraints that are always satisfied. In this way, ICM reduces the possible neighbours in each move and hence the overall search space. ICM chooses the hard constraints in such away that the space of valuations that satisfies these constraints is connected in order to guarantee that a local search can reach any solution from any valuation in this space. In this paper, we incorporate ICM into one of the most recent local search algorithm, ESG, and we show the improvement of the new algorithm. We ran this algorithm in different SAT problems.

AB - Local search (LS) methods heuristically find a solution for constraint satisfaction problems (CSP). LS starts the search for a solution from a random assignment. LS then examines the neighbours of this assignment to determine a better neighbour valuation to move to. It repeats this process of moving from the current assignment to a better assignment until it finds a solution that satisfies all constraints. ICM considers some of the constraints as hard constraints that are always satisfied. In this way, ICM reduces the possible neighbours in each move and hence the overall search space. ICM chooses the hard constraints in such away that the space of valuations that satisfies these constraints is connected in order to guarantee that a local search can reach any solution from any valuation in this space. In this paper, we incorporate ICM into one of the most recent local search algorithm, ESG, and we show the improvement of the new algorithm. We ran this algorithm in different SAT problems.

UR - http://www.scopus.com/inward/record.url?scp=32844465009&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=32844465009&partnerID=8YFLogxK

M3 - Conference contribution

SN - 1577352343

SP - 270

EP - 275

BT - Proceedings of the Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005 - Recent Advances in Artifical Intelligence

A2 - Russell, I.

A2 - Markov, Z.

ER -