Microbial strains can be manipulated to improve product yield and improve growth characteristics. Optimization algorithms are developed to identify the effects of gene knockout on the results. However, this process is often faced the problem of being trapped in local minima and slow convergence due to repetitive iterations of algorithm. In this paper, we proposed Bees Hill Flux Balance Analysis...

In an Object Relational Data Warehousing (ORDW) environment, the semantics of data and queries can be explicitly captured, represented, and utilized based on is-a and class composition hierarchies, thereby resulting in more efficient OLAP query processing. In this chapter, we show the efficacy in building semantic-rich hybrid data indexes incorporating Structural Join Index Hierarchy (SJIH) on ...

Maximum a posteriori (MAP) inference is a particularly complex type of probabilistic inference in Bayesian networks. It consists of finding the most probable configuration of a set of variables of interest given observations on a collection of other variables. In this paper we study scalable solutions to the MAP problem in hybrid Bayesian networks parameterized using conditional linear Gaussian...

Genetic algorithms have particular potential as a tool for optimization when the evaluation function is noisy. Several types of genetic algorithm are compared against a mutation driven stochastic hill-climbing algorithm on a standard set of benchmark functions which have had Gaussian noise added to them. Diierent criteria for judging the eeectiveness of the search are also considered. The genet...

Most local search algorithms are “perturbative”, incrementally moving from a search state to a neighbouring state while performing noisy hill-climbing. An alternative form of local search is “constructive”, repeatedly building partial solutions using greedy or other heuristics. Both forms have been combined with constraint propagation, and they can be hybridised with each other by perturbing pa...

Bayesian networks are frequently used to model statistical dependencies in data. Without prior knowledge of dependencies in the data, the structure of a Bayesian network is learned from the data. Bayesian network structure learning is commonly posed as an optimization problem where search is used to find structures that maximize a scoring function. Since the structure search space is superexpon...

A comparison is made of the behaviour of some evolutionary algorithms in time-varying adaptive recursive filter systems. Simulations show that an algorithm including random immigrants outperforms a more conventional algorithm using the breeder genetic algorithm as the mutation operator when the time variation is discontinuous, but neither algorithms performs well when the time variation is rapi...

The recent explosion in Internet growth has left many users awash in a sea of information, and has spurred the need for intelligent filtering systems. This paper describes work implemented in the INFOS (Intelligent News Filtering Organizational System) filtering system that is designed to reduce the user's search burden by automatically categorizing data as relevant or irrelevant based upon use...

| Delta coding is an iterative genetic search strategy that sustains search by periodically re-initializing the population. This helps to avoid premature convergence during genetic search. Delta coding also remaps hyper-space with each iteration in an attempt to locate \easier" search spaces with respect to genetic search. Here, the optimization ability of delta coding is compared against the C...

We present N-gram GP, an estimation of distribution algorithm for the evolution of linear computer programs. The algorithm learns and samples the joint probability distribution of triplets of instructions (or 3-grams) at the same time as it is learning and sampling a program length distribution. We have tested N-gram GP on symbolic regressions problems where the target function is a polynomial ...