A Population-Based Incremental Learning Algorithm with Elitist Strategy
The population-based incremental learning (PBIL) is a novel evolutionary algorithm combined the mechanisms of the Genetic Algorithm with competitive learning. In this paper, the influence of the number of selected best solutions on the convergence speed of the PBIL is studied by experiment. Based on experimental results, a PBIL algorithm with elitist strategy, named Double Learning PBIL (DLPBIL), is proposed. The new algorithm learns both the selected best solutions in current population and the optimal solution found so far in the algorithm at same time. Experimental results show that the DLPBIL out-performs the standard PBIL. Both the convergence speed and the solution quality are improved.

title: Hybrid Ant Colony Algorithm and Its Application on Function Optimization
abstract: A new hybrid ant colony algorithm was proposed. Firstly, weight factor was introduced to the binary ant colony algorithm, and then we obtained a new probability by combining probability model of Population based incremental learning (PBIL) with transfer probability of ants pheromone . The new population are produced by probability model of PBIL, transfer probability of ants pheromone and the probability of proposed algorithm so that population polymorphism is ensured and the optimal convergence rate and the ability of breaking away from the local minima are improved. Optimization simulation results based on the benchmark test functions show that the hybrid algorithm has higher convergence rate and stability than binary ant colony algorithm (BACA) and Population based incremental learning (PBIL).

title: Hybrid Algorithm Combining Ant Colony Algorithm with Genetic Algorithm for Continuous Domain
abstract: Ant colony algorithm is a kind of new heuristic biological modeling method which has the ability of parallel processing and global searching. By use of the properties of ant colony algorithm and genetic algorithm, the hybrid algorithm which adopts genetic algorithm to distribute the original pheromone is proposed to solve the continuous optimization problem. Several solutions are obtained using the ant colony algorithm through pheromone accumulation and renewal. Finally, by using crossover and mutation operation of genetic algorithm, some effective solutions are obtained. The results of experiments show better performances of the new algorithm based on six continuous test functions compared with the methods available in literature.

title: Hybrid Particle Swarm Optimization for Flow Shop Scheduling with Stochastic Processing Time
abstract: The stochastic flow shop scheduling with uncertain processing time is a typical NP-hard combinatorial optimization problem and represents an important area in production scheduling, which is difficult because of inaccurate objective estimation, huge search space, and multiple local minima. As a novel evolutionary technique, particle swarm optimization (PSO) has gained much attention and wide applications for both function and combinatorial problems, but there is no research on PSO for stochastic scheduling cases. In this paper, a class of PSO approach with simulated annealing (SA) and hypothesis test (HT), namely PSOSAHT is proposed for stochastic flow shop scheduling with uncertain processing time with respect to the makespan criterion (i.e. minimizing the maximum completion time). Simulation results demonstrate the feasibility, effectiveness and robustness of the proposed hybrid algorithm. Meanwhile, the effects of noise magnitude and number of evaluation on searching performances are also investigated.

title: Extracting Web Data Using Instance-Based Learning
abstract: This paper studies structured data extraction from Web pages, e.g., online product description pages. Existing approaches to data extraction include wrapper induction and automatic methods. In this paper, we propose an instance-based learning method, which performs extraction by comparing each new instance (or page) to be extracted with labeled instances (or pages). The key advantage of our method is that it does not need an initial set of labeled pages to learn extraction rules as in wrapper induction. Instead, the algorithm is able to start extraction from a single labeled instance (or page). Only when a new page cannot be extracted does the page need labeling. This avoids unnecessary page labeling, which solves a major problem with inductive learning (or wrapper induction), i.e., the set of labeled pages may not be representative of all other pages. The instance-based approach is very natural because structured data on the Web usually follow some fixed templates and pages of the same template usually can be extracted using a single page instance of the template. The key issue is the similarity or distance measure. Traditional measures based on the Euclidean distance or text similarity are not easily applicable in this context because items to be extracted from different pages can be entirely different. This paper proposes a novel similarity measure for the purpose, which is suitable for templated Web pages. Experimental results with product data extraction from 1200 pages in 24 diverse Web sites show that the approach is surprisingly effective. It outperforms the state-of-the-art existing systems significantly.

title: A memetic co-evolutionary differential evolution algorithm for constrained optimization
abstract: In this paper, a memetic co-evolutionary differential evolution algorithm (MCODE) for constrained optimization is proposed. Two cooperative populations are constructed and evolved by independent differential evolution (DE) algorithm. The purpose of the first population is to minimize the objective function regardless of constraints, and that of the second population is to minimize the violation of constraints regardless of the objective function. Interaction and migration happens between the two populations when separate evolutions go on for several iterations, by migrating feasible solutions into the first group, and infeasible ones into the second group. Then, a Gaussian mutation is applied to the individuals when the best solution keep unchanged for several generations. The algorithm is tested by five famous benchmark problems, and is compared with methods based on penalty functions, co-evolutionary genetic algorithm (COGA), and co-evolutionary differential evolution algorithm (CODE). The results proved the proposed cooperative MCODE is very effective and efficient.

title: Web for data mining applications
abstract: The Web not only contains a huge amount of information, but also provides a powerful infrastructure for communication and information sharing. While mining for valuable information or resources from the Web is an active research area, the authors focus on discussing the use of the Web for data mining applications. In particular, they show that the Web can be used to facilitate delivery and interpretation of a set of discovered rules.