Evolutionary Ruin And Stochastic Recreate: A Case Study On The Exam Timetabling Problem

This paper presents a new class of intelligent systems, called Evolutionary Ruin and Stochastic Recreate, that can learn and adapt to the changing enviroment. It improves the original Ruin and Recreate principle’s performance by incorporating an Evolutionary Ruin step which implements evolution within a single solution. In the proposed approach, a cycle of Solution Decomposition, Evolutionary Ruin and Stochastic Recreate continues until stopping conditions are reached. The Solution Decomposition step first uses some domain knowledge to break a solution down into its components and assign a score to each. The Evolutionary Ruin step then applies two operators (namely Selection and Mutation) to destroy a certain fraction of the entire solution. After the above steps, an input solution becomes partial and thus the resulting partial solution needs to be repaired. The repair is carried out by using the Stochastic Recreate step to reintroduce the removed items in a specific way (somewhat stochastic in order to have a better chance to jump out of the local optima), and then ask the underlying improvement heuristic whether this move will be accepted. These three steps are executed in sequence until a specific stopping condition is reached. Therefore, optimisation is achieved by solution disruption, iterative improvement and a stochastic constructive repair process performed within. Encouraging experimental results on exam timetabling problems are reported

Genetic optimization of fuzzy membership functions for cloud resource provisioning

The successful usage of fuzzy systems can be seen in many application domains owing to their capabilities to model complex systems by exploiting knowledge of domain experts. Their accuracy and performance are, however, primarily dependent on the design of its membership functions and control rules. The commonly employed technique to design membership functions is to exploit the knowledge of domain experts. However, in certain application domains, the knowledge of domain experts are limited and therefore, cannot be relied upon. Alternatively, optimization techniques such as genetic algorithms are utilized to optimize the various design parameters of fuzzy systems. In this paper, we report a case study of optimizing the membership functions of a fuzzy system using genetic algorithm, which is an important part of our recently developed cloud elasticity framework. This work aims to improve the overall performance of the framework. Results obtained from this research work demonstrate performance improvement in comparison with our previous experimental settings

Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling

This paper presents a state transition based formal framework for a new search method, called Evolutionary Ruin and Stochastic Recreate, which tries to learn and adapt to the changing environments during the search process. It improves the performance of the original Ruin and Recreate principle by embedding an additional phase of Evolutionary Ruin to mimic the survival-of-the-fittest mechanism within single solutions. This method executes a cycle of Solution Decomposition, Evolutionary Ruin, Stochastic Recreate and Solution Acceptance until a certain stopping condition is met. The Solution Decomposition phase first uses some problem-specific knowledge to decompose a complete solution into its components and assigns a score to each component. The Evolutionary Ruin phase then employs two evolutionary operators (namely Selection and Mutation) to destroy a certain fraction of the solution, and the next Stochastic Recreate phase repairs the “broken” solution. Last, the Solution Acceptance phase selects a specific strategy to determine the probability of accepting the newly generated solution. Hence, optimisation is achieved by an iterative process of component evaluation, solution disruption and stochastic constructive repair. From the state transitions point of view, this paper presents a probabilistic model and implements a Markov chain analysis on some theoretical properties of the approach. Unlike the theoretical work on genetic algorithm and simulated annealing which are based on state transitions within the space of complete assignments, our model is based on state transitions within the space of partial assignments. The exam timetabling problems are used to test the performance in solving real-world hard problems

Third-order optical nonlinearity properties of CdCl2-modifed Ge–Sb–S chalcogenide glasses

We developed a new type of chalcohalide glasses with physicochemical and nonlinear optical properties that are tunable by composition. It is found that more than 60 mol.% CdCl2 heavy metal halide can be dissolved into the ternary Ge–Sb–S system and forming stable glasses. The visible-light transparency range is extended to shorter wavelengths with the addition of CdCl2, which is beneficial for the optical quality control and infra-red (IR) system alignment. The third-order optical nonlinearity (TONL) is studied using the femtosecond Z-scan method. The results show that both the nonlinear refractive index and two photon absorption co-efficient decrease with CdCl2. Benefiting from the favorable property-tailoring effects of CdCl2, the TONL figure of merit (FOM) can be improved to meet the requirement (FOM \u3c 1) for all-optical switching and IR photonic applications

Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling

This paper presents a state transition based formal framework for a new search method, called Evolutionary Ruin and Stochastic Recreate, which tries to learn and adapt to the changing environments during the search process. It improves the performance of the original Ruin and Recreate principle by embedding an additional phase of Evolutionary Ruin to mimic the survival-of-the-fittest mechanism within single solutions. This method executes a cycle of Solution Decomposition, Evolutionary Ruin, Stochastic Recreate and Solution Acceptance until a certain stopping condition is met. The Solution Decomposition phase first uses some problem-specific knowledge to decompose a complete solution into its components and assigns a score to each component. The Evolutionary Ruin phase then employs two evolutionary operators (namely Selection and Mutation) to destroy a certain fraction of the solution, and the next Stochastic Recreate phase repairs the “broken” solution. Last, the Solution Acceptance phase selects a specific strategy to determine the probability of accepting the newly generated solution. Hence, optimisation is achieved by an iterative process of component evaluation, solution disruption and stochastic constructive repair. From the state transitions point of view, this paper presents a probabilistic model and implements a Markov chain analysis on some theoretical properties of the approach. Unlike the theoretical work on genetic algorithm and simulated annealing which are based on state transitions within the space of complete assignments, our model is based on state transitions within the space of partial assignments. The exam timetabling problems are used to test the performance in solving real-world hard problems

Atmospheric Mercury Outflow from China and Interprovincial Trade.

Mercury (Hg) is characterized by its ability to migrate between continents and its adverse effects on human health, arousing great concern around the world. The transboundary transport of large anthropogenic Hg emissions from China has attracted particular attention, especially from neighboring countries. Here, we combine an atmospheric transport model, a mass budget analysis, and a multiregional input-output model to simulate the atmospheric Hg outflow from China and investigate the impacts of Chinese interprovincial trade on the outflow. The results show outflows of 423.0 Mg of anthropogenic Hg, consisting of 65.9% of the total Chinese anthropogenic emissions, from China in 2010. Chinese interprovincial trade promotes the transfer of atmospheric outflow from the eastern terrestrial boundary (-6.4 Mg year-1) to the western terrestrial boundary (+4.5 Mg year-1) and a net decrease in the atmospheric outflow for the whole boundary, reducing the chance of risks to foreign countries derived from transboundary Hg pollution from China. These impacts of interprovincial trade will be amplified due to the expected strengthened interprovincial trade in the future. The synergistic promotional effects of interprovincial trade versus Hg controls should be considered to reduce the transboundary Hg pollution from China