GA-based Framework Research Articles

A genetic algorithm based framework for local search algorithms for distributed constraint optimization problems

Local search algorithms are widely applied in solving large-scale Distributed constraint optimization problems (DCOPs) where each agent holds a value assignment to its variable and iteratively makes a decision on whether to replace its assignment according to its neighbor states. However, the value assignments of their neighbors confine their search to a small space so that agents in local search algorithms easily fall into local optima. Fortunately, Genetic Algorithms (GAs) can direct a search process to a more promising space and help the search process to break up the confine of local states. Accordingly, we propose a GA-based framework (LSGA) to enhance local search algorithms, where a series of genetic operators are redesigned for agents in distributed scenario to accommodate DCOPs. First, a fitness function is designed to evaluate the assignments for each agent, considering the balance of local benefits and global benefits. Then, a new method is provided to decide crossover positions in terms of agent-communication and topological structure of DCOPs. Besides, a self-adaptive crossover probability and a self-adaptive mutation probability are proposed to control the uses of crossover operator and mutation operator, respectively. And more importantly, the LSGA framework can be easily applied in any local search algorithm. The experimental results demonstrate the superiority of the use of LSGA in the typical search algorithms over state-of-the-art incomplete algorithms.

An integrated algorithm for solving multi-customer joint replenishment problem with districting consideration

This paper studies a multi-customer joint replenishment problem with districting consideration (MJRPDC) which is of particular importance to a company that outsources its transportation and delivery operations to a third-party logistics (3PL) service provider. To solve the problem, we first propose an innovative search algorithm for solving the traditional multi-customer joint replenishment problem in a given zone. Then we design a GA-based framework to handle the corresponding districting problem based on the performance of each district evaluated by using the proposed search algorithm. The proposed methodologies are demonstrated by using an example of solving MJRPDC for a bank.

Yard crane scheduling problem in a container terminal considering risk caused by uncertainty

In a container terminal, the arriving times and handling volumes of the vessels are uncertain. The arriving times of the external trucks and the number of containers which are needed to be brought into or retrieved from a container terminal by external trucks within a period are also uncertain. Yard crane (YC) scheduling is under uncertainty. This paper addresses a YC scheduling problem with uncertainty of the task groups' arriving times and handling volumes. We do not only optimize the efficiency of YC operations, but also optimize the extra loss caused by uncertainty for reducing risk of adjusting schedule as the result of the task groups' arriving times and handling volumes deviating from their plan. A mathematical model is proposed for optimizing the total delay to the estimated ending time of all task groups without uncertainty and the extra loss under all uncertain scenarios. Furthermore, a GA-based framework combined with three-stage algorithm is proposed to solve the problem. Finally, the proposed mathematical model and approach are validated by numerical experiments.

Structural Optimization and Online Evolutionary Learning for Spoken Dialog Management

Designing dialog management (DM) policies that are robust to environmental noises is a nontrivial task. Approaches based on reinforcement learning (RL) are popular in academia and have been empirically shown to exhibit much better performance than handcrafted policies. However, the policies trained using RL are mostly incomprehensible, thus limiting the deployments for commercial applications. Policy optimization using genetic algorithm (GA) is a relatively new approach to spoken DM. The most notable advantage of this approach is that the trained policies can be directly interpreted by human experts. In this letter, we make several contributions to the GA-based framework. First, a structural policy learning procedure is presented. Second, a new fitness estimation method based on fitted policy evaluation is proposed. Finally, combining with these methods, an online evolutionary policy learning algorithm is designed which is much more data efficient than direct policy search using Monte Carlo simulations. These proposed approaches are empirically evaluated and compared with several state-of-the-art methods in a simulated environment. The experiments show favorable results for our approach.

The GA-based algorithms for optimizing hiding sensitive itemsets through transaction deletion

Data mining technology is used to extract useful knowledge from very large datasets, but the process of data collection and data dissemination may result in an inherent threat to privacy. Some sensitive or private information concerning individuals, businesses and organizations has to be suppressed before it is shared or published. Privacy-preserving data mining (PPDM) has become an important issue in recent years. In the past, many heuristic approaches were developed to sanitize databases for the purpose of hiding sensitive information in PPDM, but data sanitization of PPDM is considered to be an NP-hard problem. It is critical to find the balance between privacy protection for hiding sensitive information and maintaining the discovery of knowledge, or even reducing artificial knowledge in the sanitization process. In this paper, a GA-based framework with two optimization algorithms is proposed for data sanitization. A novel evaluation function with three concerned factors is designed to find the appropriate transactions to be deleted in order to hide sensitive itemsets. Experiments are then conducted to evaluate the performance of the proposed GA-based algorithms with regard to different factors such as the execution time, the number of hiding failures, the number of missing itemsets, the number of artificial itemsets, and database dissimilarity.

Minimum-phase criterion on sampling time for sampled-data interval systems using genetic algorithms

In this paper, a genetic algorithm-based approach is proposed to determine a desired sampling-time range which guarantees minimum phase behaviour for the sampled-data system of an interval plant preceded by a zero-order hold (ZOH). Based on a worst-case analysis, the identification problem of the sampling-time range is first formulated as an optimization problem, which is subsequently solved under a GA-based framework incorporating two genetic algorithms. The first genetic algorithm searches both the uncertain plant parameters and sampling time to dynamically reduce the search range for locating the desired sampling-time boundaries based on verification results from the second genetic algorithm. As a result, the desired sampling-time range ensuring minimum phase behaviour of the sampled-data interval system can be evolutionarily obtained. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature which requires undertaking a large number of evolution cycles, parallel computation for the proposed genetic algorithm is therefore proposed to accelerate the derivation process. Illustrated examples in this paper have demonstrated that the proposed GA-based approach is capable of accurately locating the boundaries of the desired sampling-time range.

A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership functions suitable for mining problems and then use the final best set of membership functions to mine fuzzy association rules. The fitness of each chromosome is evaluated by the number of large 1-itemsets generated from part of the previously proposed fuzzy mining algorithm and by the suitability of the membership functions. Experimental results also show the effectiveness of the framework.

Isolation of germ cells from rabbit fetal gonads

This paper studies a multi-customer joint replenishment problem with districting consideration (MJRPDC) which is of particular importance to a company that outsources its transportation and delivery operations to a third-party logistics (3PL) service provider. To solve the problem, we first propose an innovative search algorithm for solving the traditional multi-customer joint replenishment problem in a given zone. Then we design a GA-based framework to handle the corresponding districting problem based on the performance of each district evaluated by using the proposed search algorithm. The proposed methodologies are demonstrated by using an example of solving MJRPDC for a bank.