Patient assignment optimization in cloud healthcare systems: a distributed genetic algorithm

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

Existing music recommendation systems don't suit user subjectivity and are not naturally intuitive. We previously proposed a music recommendation system to address these problems. Our music recommendation system uses Kansei agents and a Kansei retrieval system. The Kansei agents employ a Kansei model and are characterized by a three-layered neural network. Music fluctuation properties are defined as inputs to the neural networks of the Kansei agents. Our previously proposed system returns recommendations based on user subjectivity. However, this system incurs significant user evaluation load. In this study, we introduce a distributed genetic algorithm (DGA) as an optimization method to address this problem. We compare the proposed DGA method to our previous method by simulation. The simulation results indicate that the proposed DGA method significantly outperforms previous method.

In this paper, we consider a two-level 0-1 programming problem in which there is not coordination between the decision maker (DM) at the upper level and the decision maker at the lower level. We propose a revised computational method that solves problems related to computational methods for obtaining the Stackelberg solution. Specifically, in order to improve the computational accuracy of approximate Stakelberg solutions and shorten the computational time of a computational method implementing a genetic algorithm (GA) proposed by the authors, a distributed genetic algorithm is introduced with respect to the upper level GA, which handles decision variables for the upper level DM. Parallelization of the lower level GA is also performed along with parallelization of the upper level GA. The proposed algorithm is also improved in order to eliminate unnecessary computation during operation of the lower level GA, which handles decision variables for the lower level DM. In order to verify the effectiveness of the proposed method, we propose comparisons with existing methods by performing numerical experiments to verify both the accuracy of the solution and the time required for the computation.

A major problem in the use of genetic algorithms is premature convergence. One approach for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by genetic algorithms, with each one being independent of the others. Making distinctions between the subpopulations by applying genetic algorithms with different configurations, we obtain the so-railed heterogeneous distributed genetic algorithms. These algorithms represent a promising way for introducing a correct exploration/exploitation balance in order to avoid premature convergence and reach approximate final solutions. This paper presents the gradual distributed real-coded genetic algorithms, a type of heterogeneous distributed real-coded genetic algorithms that apply a different crossover operator to each sub-population. Experimental results show that the proposals consistently outperform sequential real-coded genetic algorithms.

Discovery of Classification Rules Using Distributed Genetic Algorithm

The paper presents a distributed genetic algorithm implementation for obtaining good quality consistent results for different ordering problems. Most importantly, the solution found by the proposed Distributed GA is not only of high quality but also robust and does not require fine tuning of the probabilities of crossover and mutation. In addition, implementation of the Distributed GA is simple and does not require the use of any specialized, expensive hardware. Fault tolerance has also been provided by dynamic reconfiguration of the distributed system in the event of a process or machine failure. The effectiveness of using a simple crossover scheme with Distributed GA is demonstrated by solving three variations of the Traveling Salesman Problem (TSP).

Achieving good performance with a parallel genetic algorithm requires properly configuring control parameters such as mutation rate, crossover rate, and population size. We consider the problem of setting control parameter values in a standard, island-model distributed genetic algorithm. As an alternative to tuning parameters by hand or using a self-adaptive approach, we propose a very simple strategy which statically assigns random control parameter values to each processor. Experiments on benchmark problems show that this simple approach can yield results which are competitive with homogeneous distributed genetic algorithm using parameters tuned specifically for each of the benchmarks.

A parameter generation method based on model checking is proposed to tackle the parameter selection of hierarchical scheduling systems in Integrated Modular Avionics (IMA) by combining the classical symbolic model checking and the Statistical Model Checking (SMC). It builds a generic timed automata network to describe the temporal behavior of hierarchical systems. A distributed genetic algorithm is adopted to search the optimum partition parameters with respect to processor utilization while guaranteeing the schedulability of the system, which is formulated as safety properties of symbolic model checking and hypothesis testing of SMC. Comparing with the widely-used response time analysis, the formal model of this method is more expressive to cover complex features. The application of SMC alleviates the "state space explosion" of classical model checking. Finally, the parameter generation experiments show that the present method is able to find the global optimum solutions in the parameter space.

We present a new distributed genetic algorithm that can be used to extract useful information from distributed, large data over the network. The main idea of the proposed algorithms is to determine how many and which individuals move between subpopulations at each site adaptively. In addition, we present a method to help individuals from other subpopulations not be weeded out but adapt to the new subpopulation. We used six data sets from UCI Machine Learning Repository to compare the performance of our approach with that of the single, centralized genetic algorithm. As a result, the proposed algorithm produced better performance than the single genetic algorithm in terms of the classification accuracy with the feature subsets.

Reactive scheduling is one of the solutions for scheduling in dynamically changing manufacturing system. However, most of study for reactive scheduling use centralized scheduler. It is necessary to discuss about decentralized scheduler to catch up with nowadays huge and complex manufacturing systems. Also, it needs to be allowed that most of real manufacturing system scheduling problems are multi objective. The proposed approach gives more accurate and wider ranged solution for scheduling used distributed genetic algorithm. The effectiveness of the proposed method is confirmed by preliminary computer simulation results.

As more research centers embark on sequencing new genomes, the problem of DNA fragment assembly for shotgun sequencing is growing in importance and complexity. Accurate and fast assembly is a crucial part of any sequencing project and since the DNA fragment assembly problem is NP-hard, exact solutions are very difficult to obtain. Various heuristics, including genetic algorithms, were designed for solving the fragment assembly problem. While the sequential genetic algorithm has given good results, it is unable to sequence very large DNA molecules. In this work, we present two parallel methods, a distributed genetic algorithm and a parallel simulated annealing, to solve problem instances that are 77K base pairs long accurately

Combinatorial optimization problems are commonly tackled through the use of metaheuristics aimed to improve the way the search space is explored (diversification) and how promising areas are exploited (intensification), in order to obtain good-quality solutions. We present a distributed genetic algorithm to solve the Prize Collecting Steiner Tree Problem, a classical combinatorial optimization problem. The proposed algorithm improves the quality of the solutions by asynchronously combining distributed populations that evolve in parallel, starting from initial heterogeneous configurations. To show the effectiveness of this approach an empirical study that considers both execution time and solution quality is presented. Results show that better solutions are reached when initial populations combine configurations with high and low fitness.

This paper presents GAP/D, a VLSI implementation of a dynamic adaptation scheme for the frequency of inter-deme migration in distributed genetic algorithms (GA). Distributed GA, or multi-deme-based GA, uses multiple populations which evolve concurrently. The purpose of dynamic adaptation is to improve convergence performance so as to obtain better solutions. Through simulation experiments, we proved that our scheme achieves better performance than fixed frequency migration schemes.

Distributed Genetic Algorithms (DGAs) designed for the Internet have to take its high communication cost into consideration. For island model GAs, the migration topology has a major impact on DGA performance. This paper describes and evaluates an adaptive migration topology optimizer that keeps the communication load low while maintaining high solution quality. Experiments on benchmark problems show that the optimized topology outperforms static or random topologies of the same degree of connectivity. The applicability of the method on real-world problems is demonstrated on a hard optimization problem in VLSI design.

An unsupervised method is presented for segmenting video sequences degraded by noise. Each frame in a sequence is modeled using a Markov random field (MRF), and the energy function of each MRF is minimized by chromosomes that evolve using distributed genetic algorithms. To improve the computational efficiency, only unstable chromosomes corresponding to moving object parts are evolved. Experimental results show the effectiveness of the proposed method.