Duc Nghia Pham

A new genetic algorithm for simplified protein structure prediction

In this paper, we present a new genetic algorithm for protein structure prediction problem using face-centred cubic lattice and hydrophobic-polar energy model. Our algorithm uses i) an exhaustive generation approach to diversify the search; ii) a novel hydrophobic core-directed macro move to intensify the search; and iii) a random-walk strategy to recover from stagnation. On a set of standard benchmark proteins, our algorithm significantly outperforms the state-of-the-art algorithms for the same models.

Kangaroo: an efficient constraint-based local search system using lazy propagation

In this paper, we introduce Kangaroo, a constraint-based local search system. While existing systems such as Comet maintain invariants after every move, Kangaroo adopts a lazy strategy, updating invariants only when they are needed. Our empirical evaluation shows that Kangaroo consistently has a smaller memory footprint than Comet, and is usually significantly faster.

Modelling and solving temporal reasoning as propositional satisfiability

Representing and reasoning about time dependent information is a key research issue in many areas of computer science and artificial intelligence. One of the best known and widely used formalisms for representing interval-based qualitative temporal information is Allens interval algebra (IA). The fundamental reasoning task in IA is to find a scenario that is consistent with the given information. This problem is in general NP-complete. In this paper, we investigate how an interval-based representation, or IA network, can be encoded into a propositional formula of Boolean variables and/or predicates in decidable theories. Our task is to discover whether satisfying such a formula can be more efficient than finding a consistent scenario for the original problem. There are two basic approaches to modelling an IA network: one represents the relations between intervals as variables and the other represents the end-points of each interval as variables. By combining these two approaches with three different Boolean satisfiability (SAT) encoding schemes, we produced six encoding schemes for converting IA to SAT. In addition, we also showed how IA networks can be formulated into satisfiability modulo theories (SMT) formulae based on the quantifier-free integer difference logic (QF-IDL). These encodings were empirically studied using randomly generated IA problems of sizes ranging from 20 to 100 nodes. A general conclusion we draw from these experimental results is that encoding IA into SAT produces better results than existing approaches. More specifically, we show that the new point-based 1-D support SAT encoding of IA produces consistently better results than the other alternatives considered. In comparison with the six different SAT encodings, the SMT encoding came fourth after the point-based and interval-based 1-D support schemes and the point-based direct scheme. Further, we observe that the phase transition region maps directly from the IA encoding to each SAT or SMT encoding, but, surprisingly, the location of the hard region varies according to the encoding scheme. Our results also show a fixed performance ranking order over the various encoding schemes.

Partial weighted MaxSAT for optimal planning

We consider the problem of computing optimal plans for propositional planning problems with action costs. In the spirit of leveraging advances in general-purpose automated reasoning for that setting, we develop an approach that operates by solving a sequence of partial weighted MaxSAT problems, each of which corresponds to a step-bounded variant of the problem at hand. Our approach is the first SAT-based system in which a proof of cost-optimality is obtained using a MaxSAT procedure. It is also the first system of this kind to incorporate an admissible planning heuristic. We perform a detailed empirical evaluation of our work using benchmarks from a number of International Planning Competitions.

Memory-based local search for simplified protein structure prediction

Protein structure prediction is one of the most challenging problems in computational biology. Given a proteins amino acid sequence, a simplified version of the problem is to find an on-lattice self-avoiding walk that minimizes the interaction energy among the amino acids. In this paper, we present a memory-based local search method for the simplified problem using Hydrophobic-Polar energy model and Face Centered Cubic lattice. By memorizing local minima and then avoiding their neighbohood, our approach significantly improves the state-of-the-art local search method for protein structure prediction on a set of standard benchmark proteins.

Advances in local search for satisfiability

In this paper we describe a stochastic local search (SLS) procedure for finding satisfying models of satisfiable propositional formulae. This new algorithm, gNovelty+, draws on the features of two other WalkSAT family algorithms: R+AdaptNovelty+ and G2WSAT, while also successfully employing a dynamic local search (DLS) clause weighting heuristic to further improve performance. gNovelty+ was a Gold Medal winner in the random category of the 2007 SAT competition. In this paper we present a detailed description of the algorithm and extend the SAT competition results via an empirical study of the effects of problem structure and parameter tuning on the performance of gNovelty+. The study also compares gNovelty+ with two of the most representative WalkSAT-based solvers: G2WSAT, AdaptNovelty+, and two of the most representative DLS solvers: RSAPS and PAWS. Our new results augment the SAT competition results and show that gNovelty+ is also highly competitive in the domain of solving structured satisfiability problems in comparison with other SLS techniques.

Neighbourhood clause weight redistribution in local search for SAT

In recent years, dynamic local search (DLS) clause weighting algorithms have emerged as the local search state-of-the-art for solving propositional satisfiability problems. This paper introduces a new approach to clause weighting, known as Divide and Distribute Fixed Weights (DDFW), that transfers weights from neighbouring satisfied clauses to unsatisfied clauses in order to break out from local minima. Unlike earlier approaches, DDFW continuously redistributes a fixed quantity of weight between clauses, and so does not require a weight smoothing heuristic to control weight growth. It also exploits inherent problem structure by redistributing weights between neighbouring clauses. To evaluate our ideas, we compared DDFW with two of the best reactive local search algorithms, AdaptNovelty+ and RSAPS. In both these algorithms, a problem sensitive parameter is automatically adjusted during the search, whereas DDFW uses a fixed default parameter. Our empirical results show that DDFW has consistently better performance over a range of SAT benchmark problems. This gives a strong indication that neighbourhood weight redistribution strategies could be the key to a next generation of structure exploiting, parameter-free local search SAT solvers.

The road not taken: retreat and diverge in local search for simplified protein structure prediction

BackgroundGiven a proteins amino acid sequence, the protein structure prediction problem is to find a three dimensional structure that has the native energy level. For many decades, it has been one of the most challenging problems in computational biology. A simplified version of the problem is to find an on-lattice self-avoiding walk that minimizes the interaction energy among the amino acids. Local search methods have been preferably used in solving the protein structure prediction problem for their efficiency in finding very good solutions quickly. However, they suffer mainly from two problems: re-visitation and stagnancy.ResultsIn this paper, we present an efficient local search algorithm that deals with these two problems. During search, we select the best candidate at each iteration, but store the unexplored second best candidates in a set of elite conformations, and explore them whenever the search faces stagnation. Moreover, we propose a new non-isomorphic encoding for the protein conformations to store the conformations and to check similarity when applied with a memory based search. This new encoding helps eliminate conformations that are equivalent under rotation and translation, and thus results in better prevention of re-visitation.ConclusionOn standard benchmark proteins, our algorithm significantly outperforms the state-of-the art approaches for Hydrophobic-Polar energy models and Face Centered Cubic Lattice.

Spiral search: a hydrophobic-core directed local search for simplified PSP on 3D FCC lattice

BackgroundProtein structure prediction is an important but unsolved problem in biological science. Predicted structures vary much with energy functions and structure-mapping spaces. In our simplified ab initio protein structure prediction methods, we use hydrophobic-polar (HP) energy model for structure evaluation, and 3-dimensional face-centred-cubic lattice for structure mapping. For HP energy model, developing a compact hydrophobic-core (H-core) is essential for the progress of the search. The H-core helps find a stable structure with the lowest possible free energy.ResultsIn order to build H-cores, we present a new Spiral Search algorithm based on tabu-guided local search. Our algorithm uses a novel H-core directed guidance heuristic that squeezes the structure around a dynamic hydrophobic-core centre. We applied random walks to break premature H-cores and thus to avoid early convergence. We also used a novel relay-restart technique to handle stagnation.ConclusionsWe have tested our algorithms on a set of benchmark protein sequences. The experimental results show that our spiral search algorithm outperforms the state-of-the-art local search algorithms for simplified protein structure prediction. We also experimentally show the effectiveness of the relay-restart.

Adaptive clause weight redistribution

In recent years, dynamic local search (DLS) clause weighting algorithms have emerged as the local search state-of-the-art for solving propositional satisfiability problems. However, most DLS algorithms require the tuning of domain dependent parameters before their performance becomes competitive. If manual parameter tuning is impractical then various mechanisms have been developed that can automatically adjust a parameter value during the search. To date, the most effective adaptive clause weighting algorithm is RSAPS. However, RSAPS is unable to convincingly outperform the best non-weighting adaptive algorithm AdaptNovelty+, even though manually tuned clause weighting algorithms can routinely outperform the Novelty+ heuristic on which AdaptNovelty+ is based. In this study we introduce R+DDFW+, an enhanced version of the DDFW clause weighting algorithm developed in 2005, that not only adapts the total amount of weight according to the degree of stagnation in the search, but also incorporates the latest resolution-based preprocessing approach used by the winner of the 2005 SAT competition (R+ AdaptNovelty+). In an empirical study we show R+DDFW+ improves on DDFW and outperforms the other leading adaptive (R+Adapt-Novelty+, R+RSAPS) and non-adaptive (R+G2WSAT) local search solvers over a range of random and structured benchmark problems.