32059093
A Genetic Algorithm for Physical Mapping Problems
In this paper, we study a genetic algorithm for solving physical mapping problem. First, the physical mapping problem is transferred to an optimization problem by incorporating biological knowledge and limitations into the objective function. Based on the idea of genetic algorithms, the proposed approach integrates Edge Assembly Crossover (EAX) and Inver-over genetic operators to get the optimal solution. We analyze essential components of the proposed approach as well as implementation details. Our approach is then applied to some widely used test sets and simulated data, real data of this problem. Experimental results indicate that the new approach performs efficiently and precisely to solve physical mapping problem.
10632351
Flexible ligand docking using a robust evolutionary algorithm
A flexible ligand docking protocol based on evolutionary algorithms is investigated. The proposed approach incorporates family competition and adaptive rules to integrate decreasing-based mutations and self-adaptive mutations to act as global and local search strategies, respectively. The method is applied to a dihydrofolate reductase enzyme with the anticancer drug methotrexate and two analogues of antibacterial drug trimethoprim. Conformations and orientations closed to the crystallographically determined structures are obtained, as well as alternative structures with low energy. Numerical results indicate that the new approach is very robust. The docked lowest-energy structures have root-mean-square derivations ranging from 0.67 to 1.96 Å with respect to the corresponding crystal structures. c © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 988–998, 2000
5502812
A Continuous Genetic Algorithm for Global Optimization
40205984
A Robust Evolutionary Algorithm for Training Neural Networks
A new evolutionary algorithm is introduced for training both feedforward and recurrent neural networks. The proposed approach, called the Family Competition Evolutionary Algorithm (FCEA), automatically achieves the balance of the solution quality and convergence speed by integrating multiple mutations, family competition and adaptive rules. We experimentally analyse the proposed approach by showing that its components can cooperate with one another, and possess good local and global properties. Following the description of implementation details, our approach is then applied to several benchmark problems, including an artificial ant problem, parity problems and a two-spiral problem. Experimental results indicate that the new approach is able to stably solve these problems, and is very competitive with the comparative evolutionary algorithms.
3145676
A Genetic Algorithm with Adaptive Mutations and Family Competition for Training Neural Networks
In this paper, we present a new evolutionary technique to train three general neural networks. Based on family competition principles and adaptive rules, the proposed approach integrates decreasing-based mutations and self-adaptive mutations to collaborate with each other. Different mutations act as global and local strategies respectively to balance the trade-off between solution quality and convergence speed. Our algorithm is then applied to three different task domains: Boolean functions, regular language recognition, and artificial ant problems. Experimental results indicate that the proposed algorithm is very competitive with comparable evolutionary algorithms. We also discuss the search power of our proposed approach.
207007399
Integrating adaptive mutations and family competition into genetic algorithms as function optimizer
Abstract In this paper, we propose a robust evolutionary algorithm, called adaptive mutations genetic algorithm, for function optimization problems. Our main contribution is robustly optimizing problems whose number of variables from 2 to 200. In order to have a fair comparison, we propose the criteria for constructing a testing bed and for classifying these problems into different complexity degrees. The proposed approach, based on the family competition and multiple adaptive rules, successfully integrates the decreasing-based Gaussian mutation and self-adaptive Cauchy mutation to balance the exploitation and exploration. It is implemented and applied to widely used test functions and several nonseparable multimodal functions. Experimental results indicate that our approach is more robust than ten evolutionary algorithms.
17411195
Offer Group Generation and Delayed Processing in Multi-Issue Negotiation
In this paper we describe a mechanism for automated negotiation in which artificial agents can deal with a multi-issue negotiation in a one-to-many way based on some strategies. The negotiation process are governed dynamically by some strategies that emphasize on the diverse processing methods under the considering of different commodity attributes, including dynamic delayed evaluation and response to the received offers, generation of offer group with the same utility. Our analysis shows that the mechanism ensures that bargaining agents could reach a better agreement automatically. We also implement a prototype system to verify the advantages of our mechanism.
11069995
Power efficient encoding techniques for off-chip data buses
Reducing the power consumption of computing devices has gained a lot of attention recently. Many research works have focused on reducing power consumption in the off-chip buses as they consume a significant amount of total power. Since the bus power consumption is proportional to the switching activity, reducing the bus switching is an effective way to reduce bus power. While numerous techniques exist for reducing bus power in address buses, only a handful of techniques have been proposed for data-bus power reduction, where Frequent Value Encoding (FVE) is the best existing scheme to reduce the transition activity on the data buses.In this paper, we propose improved frequent value data-bus encoding techniques aimed at reducing more switching activity and hence, more power consumption. We propose three new schemes and five new variations to exploit bit-wise temporal and spatial locality in the data bus values. Our technique does not use additional external control signal and captures bit-wise locality to efficiently encode data values. For all the embedded and SPEC applications we tested, the overall average switching reduction is 53% over unencoded data and 11% more than the conventional FVE scheme.
1590392
Inference and learning methodology of belief-rule-based expert system for pipeline leak detection
Belief rule based expert systems are an extension of traditional rule based systems and are capable of representing more complicated causal relationships using different types of information with uncertainties. This paper describes how the belief rule based expert systems can be trained and used for pipeline leak detection. Pipeline operations under different conditions are modelled by a belief rule base using expert knowledge, which is then trained and fine tuned using pipeline operating data, and validated by testing data. All training and testing data are collected and scaled from a real pipeline. The study demonstrates that the belief rule based system is flexible, can be adapted to represent complicated expert systems, and is a valid novel approach for pipeline leak detection.
36888588
Efficient connected-index finite-length arithmetic codes
In this paper, we propose a connected-index approach to construct efficient finite-length arithmetic codes by splitting the information of the last nonfitting symbol into the current and subsequent codewords. The proposed arithmetic codes, which limit the error propagation in about one block, require neither a post-appended end-of-block symbol, nor pre-affixed side-information, to characterize the number of encoded symbols. Hence, the proposed finite-length arithmetic codes can nearly achieve the coding efficiency attained by infinite-length arithmetic codes. With high coding efficiency, limited error-propagation, and the regular process, the proposed coding approach is suitable for information exchange with small packets in modern high-speed network systems.
5516437
Compact reachability labeling for graph-structured data
Testing reachability between nodes in a graph is a well-known problem with many important applications, including knowledge representation, program analysis, and more recently, biological and ontology databases inferencing as well as XML query processing. Various approaches have been proposed to encode graph reachability information using node labeling schemes, but most existing schemes only work well for specific types of graphs. In this paper, we propose a novel approach, HLSS(Hierarchical Labeling of Sub-Structures), which identifies different types of substructures within a graph and encodes them using techniques suitable to the characteristics of each of them. We implement HLSS with an efficient two-phase algorithm, where the first phase identifies and encodes strongly connected components as well as tree substructures, and the second phase encodes the remaining reachability relationships by compressing dense rectangular submatrices in the transitive closure matrix. For the important subproblem of finding densest submatrices, we demonstrate the hardness of the problem and propose several practical algorithms. Experiments show that HLSS handles different types of graphs well, while existing approaches fall prey to graphs with substructures they are not designed to handle.