26384016
A new evolutionary approach to developing neural autonomous agents
This paper explores the use of neural networks to control robots in tasks requiring sequential and learning behavior. We propose a family competition evolutionary algorithm (FCEA) to evolve networks that can integrate these different types of behavior in a smooth and continuous manner. The approach integrates self-adaptive Gaussian mutation, self-adaptive Cauchy mutation, decreasing-based Gaussian mutation, and family competition. In order to illustrate the power of the approach, we apply this approach to two different task domains: the "artificial ant" problem and a sequential behavior problem - an agent learns to play football. From the experimental results, we find our approach performs much better than other evolutionary algorithms in these two tasks. Based on the results from our experiments, it is shown that our approach can evolve neural networks to provide a means of integrating, sequencing and learning within a single control system.
5502812
A Continuous Genetic Algorithm for Global Optimization
5717822
A family competition evolutionary algorithm for automated docking of flexible ligands to proteins
We study an evolutionary algorithm for flexible ligand docking. Based on family competition and adaptive rules, the proposed approach consists of global and local strategies by integrating decreasing mutations and self-adaptive mutations. To demonstrate the robustness of the proposed approach, we apply it to the problems of the first international contests on evolutionary optimization. Following the description of function optimization, our approach is applied to a dihydrofolate reductase enzyme with the anti-cancer drug methotrexate and with two analogs of the antibacterial drug trimethoprim. Our numerical results indicate that the proposed approach is robust. The docked lowest energy structures have rms derivations ranging from 0.72 A to 1.98 A with respect to the corresponding crystal structure.
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.
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.
252049
MECA: an extensible, expressive system and language for statically checking security properties
This paper describes a system and annotation language, MECA, for checking security rules. MECA is expressive and designed for checking real systems. It provides a variety of practical constructs to effectively annotate large bodies of code. For example, it allows programmers to write programmatic annotators that automatically annotate large bodies of source code. As another example, it lets programmers use general predicates to determine if an annotation is applied; we have used this ability to easily handle kernel backdoors and other false-positive inducing constructs. Once code is annotated, MECA propagates annotations aggressively, allowing a single manual annotation to derive many additional annotations (e.g., over one hundred in our experiments) freeing programmers from the heavy manual effort required by most past systems.MECA is effective. Our most thorough case study was a user-pointer checker that used 75 annotations to check thousands of declarations in millions of lines of code in the Linux system. It found over forty errors, many of which were serious, while only having eight false positives.
3829528
Dynamically inferring temporal properties
Model checking requires a specification of the target system's desirable properties, some of which are temporal. Formulating a temporal property of the system based on either its abstract model or implementation requires a deep understanding of its behavior and sophisticated knowledge of the chosen formalism. This has been a major impediment to documenting and verifying temporal properties. We propose a dynamic approach to automatically infer a program's temporal properties based on a set of property pattern templates. We describe a preliminary implementation of this approach, and report on our experience using it to discover interesting temporal properties of a small program.
46223238
Environmental impact assessment using the evidential reasoning approach
Environmental impact assessment (EIA) problems are often characterised by a large number of identified environmental factors that are qualitative in nature and can only be assessed on the basis of human judgments, which inevitably involve various types of uncertainties such as ignorance and fuzziness. So, EIA problems need to be modelled and analysed using methods that can handle uncertainties. The evidential reasoning (ER) approach provides such a modelling framework and analysis method. In this paper the ER approach will be applied to conduct EIA analysis for the first time. The environmental impact consequences are characterized by a set of assessment grades that are assumed to be collectively exhaustive and mutually exclusive. All assessment information, quantitative or qualitative, complete or incomplete, and precise or imprecise, is modelled using a unified framework of a belief structure. The original ER approach with a recursive ER algorithm will be introduced and a new analytical ER algorithm will be investigated which provides a means for using the ER approach in decision situations where an explicit ER aggregation function is needed such as in optimisation problems. The ER approach will be used to aggregate multiple environmental factors, resulting in an aggregated distributed assessment for each alternative policy. A numerical example and its modified version are studied to illustrate the detailed implementation process of the ER approach and demonstrate its potential applications in EIA.
7776820
Scalable continuous query processing by tracking hotspots
This paper considers the problem of scalably processing a large number of continuous queries. We propose a flexible framework with novel data structures and algorithms for group-processing and indexing continuous queries by exploiting potential overlaps in query predicates. Our approach partitions the collection of continuous queries into groups based on the clustering patterns of the query ranges, and then applies specialized processing strategies to those heavily-clustered groups (or hotspots). To maintain the partition dynamically, we present efficient algorithms that maintain a nearly optimal partition in nearly amortized logarithmic time. We show how to use the hotspots to scalably process large numbers of continuous select-join and band-join queries, which are much more challenging than simple range selection queries. Experiments demonstrate that this approach can improve the processing throughput by orders of magnitude. As another application of hotspots, we show how to use them to build a high-quality histogram for intervals in linear time.
1721071
Incremental maintenance of XML structural indexes
Increasing popularity of XML in recent years has generated much interest in query processing over graph-structured data. To support efficient evaluation of path expressions, many structural indexes have been proposed. The most popular ones are the 1-index, based on the notion of graph bisimilarity, and the recently proposed A(k)-index, based on the notion of local similarity to provide a trade-off between index size and query answering power. For these indexes to be practical, we need effective and efficient incremental maintenance algorithms to keep them consistent with the underlying data. However, existing update algorithms for structural indexes essentially provide no guarantees on the quality of the index; the updated index is usually larger size than necessary, degrading the performance for subsequent queries.In this paper, we propose update algorithms for the 1-index and the A(k)-index with provable guarantees on the resulting index quality. Our algorithms always maintain a minimal index, i.e., merging any two index nodes would result in an incorrect index. For the 1-index, if the data graph is acyclic, our algorithm further ensures that the index is minimum, i.e., it has the least number of index nodes possible. For the A(k)-index, we show that the minimal index our algorithm maintains is also the unique minimum A(k)-index, for both acyclic and cyclic data graphs. Finally, through experimental evaluation, we demonstrate that our algorithms bring significant improvement over previous methods, in terms of both index size and update time.