Computational Intelligence Meets the Game of Go @ IEEE WCCI 2012

International audienceSince 2008, National University of Tainan (NUTN) in Taiwan and other academic organizations have hosted or organized several human vs. computer Go-related events [1, 2, 3, 4, 5] in Taiwan and in IEEE CIS flag conferences, including FUZZ-IEEE 2009, IEEE WCCI 2010, IEEE SSCI 2011, and FUZZ-IEEE 2011. Chun- Hsun Chou (9P), Ping-Chiang Chou (5P), Joanne Missingham (6P), Shang- Rong Tsai (6D), Sheng-Shu Chang (6D), and Shi-Jim Yen (6D) were invit- ed to attend the Human vs. Computer Go Competition @ IEEE WCCI 2012 (http://oase.nutn.edu.tw/wcci2012/ and http://top.twman.org/wcci2012) held in Brisbane, Australia, in June 2012

Sensor deployment for air pollution monitoring using public transportation system

IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012 hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)Air pollution monitoring is a very popular research topic and many monitoring systems have been developed. In this paper, we formulate the Bus Sensor Deployment Problem (BSDP) to select the bus routes on which sensors are deployed, and we use Chemical Reaction Optimization (CRO) to solve BSDP. CRO is a recently proposed metaheuristic designed to solve a wide range of optimization problems. Using the real world data, namely Hong Kong Island bus route data, we perform a series of simulations and the results show that CRO is capable of solving this optimization problem efficiently. © 2012 IEEE.published_or_final_versio

Real-coded chemical reaction optimization with different perturbation functions

IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012 hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)Chemical Reaction Optimization (CRO) is a powerful metaheuristic which mimics the interactions of molecules in chemical reactions to search for the global optimum. The perturbation function greatly influences the performance of CRO on solving different continuous problems. In this paper, we study four different probability distributions, namely, the Gaussian distribution, the Cauchy distribution, the exponential distribution, and a modified Rayleigh distribution, for the perturbation function of CRO. Different distributions have different impacts on the solutions. The distributions are tested by a set of wellknown benchmark functions and simulation results show that problems with different characteristics have different preference on the distribution function. Our study gives guidelines to design CRO for different types of optimization problems. © 2012 IEEE.published_or_final_versio

Chemical reaction optimization for the fuzzy rule learning problem

IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012 hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)In this paper, we utilize Chemical Reaction Optimization (CRO), a newly proposed metaheuristic for global optimization, to design Fuzzy Rule-Based Systems (FRBSs). CRO imitates the interactions of molecules in a chemical reaction. The molecular structure corresponds to a solution, and the potential energy is analogous to the objective function value. Molecules are driven toward the lowest energy stable state, which corresponds to the global optimum of the problem. In the realm of modeling with fuzzy rule-based systems, automatic derivation of fuzzy rules from numerical data plays a critical role. We propose to use CRO with Cooperative Rules (COR) to solve the fuzzy rule learning problem in FRBS. We formulate the learning process of FRBS in the form of a combinatorial optimization problem. Our proposed method COR-CRO is evaluated by two fuzzy modeling benchmarks and compared with other learning algorithms. Simulation results demonstrate that COR-CRO is highly competitive and outperforms many other existing optimization methods. © 2012 IEEE.published_or_final_versio

Short adjacent repeat identification based on chemical reaction optimization

IEEE World Congress on Computational Intelligence (WCCI 2012), Brisbane, Australia, 10-15 June 2012 hosted three conferences: the 2012 International Joint Conference on Neural Networks (IJCNN 2012), the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), and the 2012 IEEE Congress on Evolutionary Computation (IEEE CEC 2012)The analysis of short tandem repeats (STRs) in DNA sequences has become an attractive method for determining the genetic profile of an individual. Here we focus on a more general and practical issue named short adjacent repeats identification problem (SARIP), which is extended from STR by allowing short gaps between neighboring units. Presently, the best available solution to SARIP is BASARD, which uses Markov chain Monte Carlo algorithms to determine the posterior estimate. However, the computational complexity and the tendency to get stuck in a local mode lower the efficiency of BASARD and impede its wide application. In this paper, we prove that SARIP is NP-hard, and we also solve it with Chemical Reaction Optimization (CRO), a recently developed metaheuristic approach. CRO mimics the interactions of molecules in a chemical reaction and it can explore the solution space efficiently to find the optimal or near optimal solution(s). We test the CRO algorithm with both synthetic and real data, and compare its performance in mode searching with BASARD. Simulation results show that CRO enjoys dozens of times, or even a hundred times shorter computational time compared with BASARD. It is also demonstrated that CRO can obtain the global optima most of the time. Moreover, CRO is more stable in different runs, which is of great importance in practical use. Thus, CRO is by far the best method on SARIP. © 2012 IEEE.published_or_final_versio

Forecasting of Medium-term Rainfall Using Artificial Neural Networks: Case Studies from Eastern Australia

The advent of machine learning, of which artificial neural networks (ANN) are a component, has provided an opportunity for improved rainfall forecasts, which is of value for water infrastructure management, agriculture, mining and other industries. In this chapter, ANNs are shown to provide more skillful monthly rainfall forecasts for locations in south-eastern Queensland, Australia, for lead-times of 3–12 months. The skill of the forecasts from the ANNs is highest when the models are individually optimized for each month, and when longer-duration series are used as input. The ANN technique has application where there is temperature and rainfall data extending back at least 50 years. Such datasets exist for much of Europe and North America, though a review of the available literature indicates most research into the application of ANN has focused on China, India and Australia

Web Usage Mining with Evolutionary Extraction of Temporal Fuzzy Association Rules

In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets' boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules. (C) 2013 Elsevier B.V. All rights reserved

Discerning the Role Context Plays in the Value of Information

For the military, effective human-agent teaming requires a shared understanding between the human and the intelligent agents acting on their behalf. One of the central challenges associated with developing this shared understanding originates at the information level. The simple fact is while all information may be created equal, the value of information is not. Confounding this calculation is the knowledge that the true value of information is dependent not only on its source, content and latency, but just as importantly on the context of the situation in which it is being exercised. Building upon previous research aimed at codifying the value of information, this paper presents a multi-facetted experiment meant to discern a Soldier’s value of information within varying military contexts. Initial results reveal that context plays a significant role in how information is valued and more importantly provides a foundation for strengthening human-agent information understanding and collaboration