Computing Binary Combinatorial Gray Codes Via Exhaustive Search With SAT Solvers

Zinovik, Igor; Kroening, Daniel; Chebiryak, Yury

doi:10.1109/tit.2008.917695

Cited by 17 publications

(17 citation statements)

References 23 publications

(31 reference statements)

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“…Furthermore, the brute force algorithm will explore any combinations of assignments and directly compute the total number of satisfied clauses. Technically, the brute force algorithm will allow our paradigm to hunt for the total satisfied clauses brutally, even in tremendous search dimension [25]. Specifically, the brute force search will evaluate the candidate solutions clause by clause in order to obtain the feasible solution.…”

Section: A Brute Force (Bf) Algorithmmentioning

confidence: 99%

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Robust Artificial Immune System in the Hopfield network for Maximum k-Satisfiability

Mansor¹,

Kasihmuddin²,

Sathasivam³

2017

IJIMAI

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-Artificial Immune System (AIS) algorithm is a novel and vibrant computational paradigm, enthused by the biological immune system. Over the last few years, the artificial immune system has been sprouting to solve numerous computational and combinatorial optimization problems. In this paper, we introduce the restricted MAX-kSAT as a constraint optimization problem that can be solved by a robust computational technique. Hence, we will implement the artificial immune system algorithm incorporated with the Hopfield neural network to solve the restricted MAX-kSAT problem. The proposed paradigm will be compared with the traditional method, Brute force search algorithm integrated with Hopfield neural network. The results demonstrate that the artificial immune system integrated with Hopfield network outperforms the conventional Hopfield network in solving restricted MAX-kSAT. All in all, the result has provided a concrete evidence of the effectiveness of our proposed paradigm to be applied in other constraint optimization problem. The work presented here has many profound implications for future studies to counter the variety of satisfiability problem.

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Section: A Brute Force (Bf) Algorithmmentioning

confidence: 99%

“…Hence, it can be considered as the primitive searching tool for the standalone Hopfield neural network. Therefore, the brute force searching method is practically easy to implement by the researchers [25].…”

Section: A Brute Force (Bf) Algorithmmentioning

confidence: 99%

Robust Artificial Immune System in the Hopfield network for Maximum k-Satisfiability

Mansor¹,

Kasihmuddin²,

Sathasivam³

2017

IJIMAI

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“…Subsequently, exhaustive search devours more computation time in searching for the maximum number of satisfied clauses completely [28]. In this paper, we will generate random bit strings and compute the number of satisfied clauses directly, clause by clause.…”

Section: A Exhaustive Search (Es)mentioning

confidence: 99%

“…Henceforth, the correct assignment will be stored into the Hopfield's artificial brain in the form of content addressable memory (CAM). Some related work on exhaustive search has been done by a few neural network practicioners such as Aiman & Asrar [27], Kaushik [28], and Zinovik et al [32]. In this paper, we hybridized ES algorithm with the Hopfield neural network as a network based on logic programming to solve MAX-kSAT problems (HNN-MAX2SATES and HNN-MAX3SATES).…”

Section: A Exhaustive Search (Es)mentioning

confidence: 99%

Genetic Algorithm for Restricted Maximum k-Satisfiability in the Hopfield Network

Kasihmuddin¹,

Mansor²,

Sathasivam³

2016

IJIMAI

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-The restricted Maximum k-Satisfiability MAXkSAT is an enhanced Boolean satisfiability counterpart that has attracted numerous amount of research. Genetic algorithm has been the prominent optimization heuristic algorithm to solve constraint optimization problem. The core motivation of this paper is to introduce Hopfield network incorporated with genetic algorithm in solving MAX-kSAT problem. Genetic algorithm will be integrated with Hopfield network as a single network. The proposed method will be compared with the conventional Hopfield network. The results demonstrate that Hopfield network with genetic algorithm outperforms conventional Hopfield networks. Furthermore, the outcome had provided a solid evidence of the robustness of our proposed algorithms to be used in other satisfiability problem.

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“…Paterson and Tuliani presented a construction method based on binary necklaces [17], generalizing ideas for obtaining single-track circuit codes of [7]. In earlier work, we improved lower bounds and proved optimality of circuit codes for 14 different sets of parameters (n, δ) [24]. The approach uses a SAT-solver and is not limited to specific values of a spread.…”

Section: Introductionmentioning

confidence: 99%

An efficient SAT encoding of circuit codes

Chebiryak

Kroening

2008

2008 International Symposium on Information Theory and Its Applications

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Circuit codes in hypercubes are generalized snake-inthe-box codes and are used in analog-to-digital conversion devices. The construction of the longest known circuit codes is based on either an exhaustive search or an algorithm that restricts the search to the codes with periodic coordinate sequences. In this paper, we describe an efficient SAT encoding of circuit codes, which enabled us to obtain new circuit codes.

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Computing Binary Combinatorial Gray Codes Via Exhaustive Search With SAT Solvers

Cited by 17 publications

References 23 publications

Robust Artificial Immune System in the Hopfield network for Maximum k-Satisfiability

Robust Artificial Immune System in the Hopfield network for Maximum k-Satisfiability

Genetic Algorithm for Restricted Maximum k-Satisfiability in the Hopfield Network

An efficient SAT encoding of circuit codes

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