Joc Cing Tay

Institute of Electrical and Electronics Engineers | IEEE

Sexual selection has been mathematically modeled using quantitative genetics as well as population genetics. Two-locus simulation models have been used to study the evolution of male display and female preference. We present an individual-based simulation model of sexual selection in a quantitative genetic context. We show that under certain condit...

Many mathematical and computational models have been developed to investigate the complexity of HIV dynamics, immune response and drug therapy. However, there are not many models which consider the dynamics of virus intracellular replication at a single level. We propose a model of HIV intracellular replication where infected cells undergo a single...

Background: Hand hygiene adherence and staffing levels are known to impact nosocomial MRSA transmissions. Objective: We wish to quantify their effect given host heterogeneities, contact patterns, existing interventions and the environmental topology. Methods: Over two weeks of cross-sectional study, together with automated movement tracking, an...

With the recent introduction of third generation (3G) technology in the field of mobile communications, mobile phone service providers will have to find an effective strategy to market this new technology. One approach is to analyze the current profile of existing 3G subscribers to discover common patterns in their usage of mobile phones. With thes...

With the recent introduction of third generation (3G) technology in the field of mobile communications, mobile phone service providers will have to find an effective strategy to market this new technology. One approach is to analyze the current profile of existing 3G subscribers to discover common patterns in their usage of mobile phones. With thes...

Finding realistic schedules for flexible job shop problems has attracted many researchers recently due to its nondeterministic polynomial time (NP) hardness. In this paper, we present an efficient approach for solving the multiple-objective flexible job shop by combining evolutionary algorithm and guided local search (GLS). Instead of applying rand...

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise...

Traditional approaches in epidemiological modeling assume a fully mixed population with uniform contact rates. These assumptions are inaccurate in a real epidemic. We propose an agent-based and spatially explicit epidemiological model to simulate the spread of influenza for nosocomial environments with high heterogeneity in interactions and suscept...

We solve the multi-objective flexible job-shop problems by using dispatching rules discovered through genetic programming. While Simple Priority Rules have been widely applied in practice, their efficacy remains poor due to lack of a global view. Composite dispatching rules have been shown to be more effective as they are constructed through human...

The game of tag is frequently used in the study of pursuit and evasion strategies that are discovered through competitive coevolution. The aim of coevolution is to create an arms race where opposing populations cyclically evolve in incremental improvements, driving the system towards better strategies. A coevolutionary simulation of the game of tag...

Multi-agent (or MA) -based design approaches have received much research attention lately for modeling immunological systems due to their efficacy in representing non-heterogeneous behaviors in the population under dynamic environmental and topological conditions. The update scheme of a MA model refers to the frequency of agent state updates and ho...

Even when the question is well-posed, it is often difficult to determine an appropriate level of detail in a multi-agent model for any complex system, therefore in practice, frequent revisions on model granularity become inevitable. Ideally, we would like a modeling methodology that allows small and incremental changes in granularity. This allows d...

With the recent introduction of third generation (3G) technology in the field of mobile communications, mobile phone service providers will have to find an effective strategy to market this new technology. One approach is to analyze the current profile of existing 3G subscribers to discover common patterns in their usage of mobile phones. With thes...

With the recent introduction of third generation (3G) technology in the field of mobile commu-nications, mobile phone service providers will have to find an effective strategy to market this new technology. One approach is to analyze the current profile of existing 3G subscribers to discover common patterns in their usage of mobile phones. With the...

Finding realistic schedules for Flexible Job Shop Problems has attracted many researchers recently due to its NP-hardness. In this paper, we present an efficient approach for solving the multi- objective flexible job shop by combining Evolutionary Algorithm and Guided Local Search. Instead of applying random local search to find neighborhood soluti...

In recent years, the interaction between evolution and learning has received much attention from the research community. Some recent studies on machine learning have shown that it can significantly improve the efficiency of problem solving when using evolutionary algorithms. This paper proposes an architecture for learning and evolving of Flexible...

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. These choices need not necessarily be mutually exclusive. We propose a hybrid agent-based approach where biological cells are modeled as individuals (agents) while chemical molecules are kept as quantities. Th...

We approximate optimal solutions to the Flexible Job-Shop Problem by using dispatching rules discovered through Genetic Programming. While Simple Priority Rules have been widely applied in practice, their efficacy remains poor due to lack of a global view. Composite Dispatching Rules have been shown to be more effective as they are constructed thro...

With the recent introduction of third generation (3G) technology in the field of mobile communications, mobile phone service providers will have to find an effective strategy to market this new technology. One approach is to analyze the current profile of existing 3G subscribers to discover common patterns in their usage of mobile phones. With thes...

We use probabilistic Boolean networks to simulate the pathogenesis of Dengue Hemorraghic Fever (DHF). Based on Chaturvedi's work, the strength of cytokine influences are modeled stochastically as inducement probabilities. We use an aggregated function approach to derive the DHF Infection Model. Two basins of attractors are observed with synchronous...

We use probabilistic boolean networks to simulate the pathogenesis of Dengue Hemorraghic Fever (DHF). Based on Chaturvedi's work, the strength of cytokine influences are modeled stochastically as inducement probabilities. Two basins of attractors are observed with synchronous updating; the Null Infection cycle attractor shows an expected cross-regu...

We propose a hybrid algorithm (called ALPINE) between Genetic Algorithm and Dantzig's Simplex method to approximate optimal solutions for the Flexible Job-Shop Problem. Locally, Simplex is extended for the JSP linear program to reduce the number of infeasible solutions while solution quality is improved with an operation order search. Globally, a n...

The impact of learning on evolution in dynamic environments undergoes recognized stages of the Baldwin Effect although its cause is not clear. To identify it experimentally, we devise spatial constraints and allowed autonomous reproduction for a multi-agent game play using Iterated Prisoner's Dilemma. In comparison to Arita and Suzuki's model, we e...

The interaction between evolution and learning has received much attention with recent studies in machine learning showing that it can significantly improve the efficiency of evolutionary strategies for job-shop scheduling. We propose a tripartite architecture called LEGA; comprising a population generator that improves the quality of the initial p...

We solve the flexible job shop problem (FJSP) by using dispatching rules discovered through genetic programming (GP). While simple priority rules (SPR) have been widely applied in practice, their efficacy remains poor due to lack of a global view. Composite dispatching rules (CDR) have been shown to be more effective as they are constructed through...

We apply the Clonal Selection principle of the human immune system to solve the Flexible Job-Shop Problem with recirculation. Various practical design issues are addressed in the implemented algorithm, ClonaFLEX; first, an efficient antibody representation which creates only feasible solutions and a bootstrapping antibody initialization method to r...

Considerable research effort has provided mathematical and computational models of the human immune response under viral infection. However, the quality of simulated results are highly dependent on the choice of modeling strategy. We examine two modeling approaches of HIV pathogenesis: Mathematical and Multi-Agent (or MA) Models. The latter has rel...

Researchers of HIV-1 are today, still unable to determine exactly the biological mechanisms that cause AIDS. Various mechanisms have been hypothesized and their existences have been experimentally verified, but whether they are sufficient to account for the observed disease progression is still in question. To better understand the phenomena, HIV-1...

Currently most reported immune system simulations in literature involve the use of differential equations, genetic algorithm-based searching or simple cellular automata models. This limits the diversity in results obtained and thus provides fewer avenues for experimenting with behavioral responses of the immune system entities under exogenous stimu...

Rules provide a flexible method of recognizing events and event patterns through the matching of CDR data fields. The first step in automatic CDR filtering is to identify the data fields that comprise the CDR format. In the particular case of the Nortel Meridian One PABX, five different call data types can be identified that are critical for call r...

This work presents an efficient methodology called GENACE for solving the flexible job-shop scheduling problem (or FJSP) with recirculation. We show how CDRs are used to solve the FJSP with recirculation by themselves and to provide a bootstrapping mechanism to initialize GENACE. We then adopt a cultural evolutionary architecture to maintain knowle...

As the Flexible Job Shop Scheduling Problem (or FJSP) is strongly NP-hard, using an evolutionary approach to find near-optimal solutions requires effective chromosome representations as well as carefully designed parameters for crossover and mutation to achieve efficient search. This paper proposes a new chromosome representation and a design of re...

The default pattern matching capabilities in today’s RDBMS are generally unable to cope with errors and variations that may exist in stored textual information. In this paper, we present SKIPPER, a simple search methodology that allows approximate string matching on multiple-attribute, large-scale customer address information for the Credit Collect...

The reason maintenance problem-solving architecture presents a new paradigm for organizing data and control. This architecture provides an improvement over traditional pattern-directed inference systems in the five areas of artificial intelligence (AI) program design. These areas are efficiency, coherency, flexibility, additivity, and extendibility...

The richness of the constraint satisfaction problem (or CSP) in representing combinatorial search maladies has resulted in a torrent of techniques for efficiently solving them. These techniques have focused on discovering better backtrack points, learning from dead-ends and avoiding repetitious interference, problem reduction method and the use of...

Part I (see ibid., pp. 414-421) provided a rigorous definition of the CSL (constraint specification language) algebra as a language to model the general n-ary logical constraint satisfaction problem (LCSP). In this paper, the majority of our discussions focus on design and implementational issues that arose while building software for compiling the...

Examines and details the motivations and design of a constraint specification language (CSL) for user-defined constraints. Many approaches to constraint programming are based on, as well as extended from, the logic programming paradigm. Some of the better-known constraint logic programming languages and systems are CLP, PROLOG III and CHIP. In thes...

This paper presents a performance measurement architecture for objectively evaluating constraint atisfaction techniques. It examines and analyses the overheads involved in using the assumption-based dependency directed backtracking for solving constraint satisfaction problems. The problem of using a functional representation of contraints in the ev...

Research effort in constraint satisfaction has traditionally been devoted to curbing the exponential cost of search through the methods of backtracking and problem reduction. These methods serve the overall goal of avoiding redundant computations and reduce the search space needed to derive a solution. The advent of reason maintenance systems (or R...