遗传算法结合蚁群算法电力系统故障浅析

根据蚁群算法与遗传算法的特性,提出了与遗传算法混合的蚁群算法（G3A）：由遗传算法生成初始信息素分布,在蚁群算法寻优中,利用蚁群算法信息素轨迹更新求精确解,保持了遗传算法的全面搜索能力,从而获得在时间效率与精解效率都更优的一种新的启发式方法。     

Quantum programming languages

After giving a bird’s view of some existing quantum programming languages, this paper reports the recent results made by the quantum computation group of the State Key Laboratory for Novel Software Technology and the Department of Computer Science and Technology at Nanjing University, i.e., the quantum programming languages NDQJava, NDQFP and their processing systems.     

Incremental Evolution in ANNs: Neural Nets which Grow

This paper explains the optimisation of neuralnetwork topology using Incremental Evolution;that is, by allowing the network to expand byadding to its structure. This method allows anetwork to grow from a simple to a complexstructure until it is capable of fulfilling itsintended function. The approach is somewhatanalogous to the growth of an embryo or theevolution of a fossil line through time, it istherefore sometimes referred to as anembryology or embryological algorithm. Thepaper begins with a general introduction,comparing this method to other competingtechniques such as The Genetic Algorithm, otherEvolutionary Algorithms and SimulatedAnnealing. A literature survey of previous workis included, followed by an extensive newframework for application of the technique.Finally, examples of applications and a generaldiscussion are presented.     

A Review of the Application of Meta-Heuristic Algorithms to 2D Strip Packing Problems

This paper is a review of the approachesdeveloped to solve 2D packing problems withmeta-heuristic algorithms. As packing tasks arecombinatorial problems with very large searchspaces, the recent literature encourages theuse of meta-heuristic search methods, inparticular genetic algorithms. The objective ofthis paper is to present and categorise thesolution approaches in the literature for 2Dregular and irregular strip packing problems.The focus is hereby on the analysis of themethods involving genetic algorithms. Anoverview of the methods applying othermeta-heuristic algorithms including simulatedannealing, tabu search, and artificial neuralnetworks is also given.     

On the Convergence of Temporal-Difference Learning with Linear Function Approximation

The asymptotic properties of temporal-difference learning algorithms with linear function approximation are analyzed in this paper. The analysis is carried out in the context of the approximation of a discounted cost-to-go function associated with an uncontrolled Markov chain with an uncountable finite-dimensional state-space. Under mild conditions, the almost sure convergence of temporal-difference learning algorithms with linear function approximation is established and an upper bound for their asymptotic approximation error is determined. The obtained results are a generalization and extension of the existing results related to the asymptotic behavior of temporal-difference learning. Moreover, they cover cases to which the existing results cannot be applied, while the adopted assumptions seem to be the weakest possible under which the almost sure convergence of temporal-difference learning algorithms is still possible to be demonstrated.     

Optimizing Epochal Evolutionary Search: Population-Size Dependent Theory

Epochal dynamics, in which long periods of stasis in an evolving population are punctuated by a sudden burst of change, is a common behavior in both natural and artificial evolutionary processes. We analyze the population dynamics for a class of fitness functions that exhibit epochal behavior using a mathematical framework developed recently, which incorporates techniques from the fields of mathematical population genetics, molecular evolution theory, and statistical mechanics. Our analysis predicts the total number of fitness function evaluations to reach the global optimum as a function of mutation rate, population size, and the parameters specifying the fitness function. This allows us to determine the optimal evolutionary parameter settings for this class of fitness functions.We identify a generalized error threshold that smoothly bounds the two-dimensional regime of mutation rates and population sizes for which epochal evolutionary search operates most efficiently. Specifically, we analyze the dynamics of epoch destabilization under finite-population sampling fluctuations and show how the evolutionary parameters effectively introduce a coarse graining of the fitness function. More generally, we find that the optimal parameter settings for epochal evolutionary search correspond to behavioral regimes in which the consecutive epochs are marginally stable against the sampling fluctuations. Our results suggest that in order to achieve optimal search, one should set evolutionary parameters such that the coarse graining of the fitness function induced by the sampling fluctuations is just large enough to hide local optima.     

A metagenetic algorithm for information filtering and collection from the World Wide Web

This paper describes the implementation of evolutionary techniques for information filtering and collection from the World Wide Web. We consider the problem of building intelligent agents to facilitate a person's search for information on the Web. An intelligent agent has been developed that uses a metagenetic algorithm in order to collect and recommend Web pages that will be interesting to the user. The user's feedback on the agent's recommendations drives the learning process to adapt the user's profile with his/her interests. The software agent utilizes the metagenetic algorithm to explore the search space of user interests. Experimental results are presented in order to demonstrate the suitability of the metagenetic algorithm's approach on the Web.     

A Theoretical Approach of an Intelligent Robot Gripper to Grasp Polygon Shaped Objects

This paper presents an approach for grasp planning and grasp forces optimization of polygon shaped objects. The proposed approach is an intelligent rule-based method that figures out the minimal number of fingers and minimal values of contact forces. These fingers are required to securely grasp a rigid body in the presence of friction and under the action of some external force. This is accomplished by finding optimal contact points on the object boundary along with minimal number of fingers required for achieving the aforementioned goal. Our system handles every object case independently. It generates a rule base for each object based on adequate values of external forces. The system uses the genetic algorithm as its search mechanism, and a rule evaluation mechanism called bucket brigade for the reinforcement learning of the rules. The process mainly consists of two stages; learning then retrieval. Retrievals act on line utilizing previous knowledge and experience embedded in a rule base. If retrievals fail in some cases, learning is presumed until that case is resolved. The algorithm is very general and can be adapted for interface with any object shape. The resulting rule base varies in size according to the degree of difficulty and dimensionality of the grasping problem.     

A Two-Layer Robot Controller Design Using Evolutionary Algorithms

The results obtained by a rule-based proportional, integral, derivative (PID) precompensator controller applied to a two-joint manipulator are discussed. The end effector is made to follow a specified trajectory obtained from the inverse kinematics by an appropriate design of a fuzzy control law. The desired trajectory is determined by the values of the joint variables and the structural kinematics parameters of the manipulator. The performance of the PID controller is exploited here to build a fuzzy precompensator that will enhance the conventional PID and to obtain better performances and results. The fuzzy rule base of the precompensator designed is found by associating two evolutionary algorithms that search for the optimal solution.