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An Efficient Segmentation Algorithm for Entity Biodiversity Informatics, 6, 2009, pp. 5-17 AN EFFICIENT SEGMENTATION ALGORITHM FOR ENTITY INTERACTION EUGENE CH’NG 1 School of Computing and Information Technology, The University of Wolverhampton Wulfruna Street, WV1 1SB, Wolverhampton, United Kingdom. Abstract .—The inventorying of biological diversity and studies in biocomplexity require the management of large electronic datasets of organisms. While species inventory has adopted structured electronic databases for some time, the computer modelling of the functional interactions between biological entities at all levels of life is still in the stage of development. One of the challenges for this type of modelling is the biotic interactions that occur between large datasets of entities represented as computer algorithms. In real-time simulation that models the biotic interactions of large population datasets, the use of computational processing time could be extensive. One way of increasing the efficiency of such simulation is to partition the landscape so that entities need only traverse its local space for entities that falls within the interaction proximity. This article presents an efficient segmentation algorithm for biotic interactions for research related to the modelling and simulation of biological systems. Key words.— artificial life, entity interaction, individual-based model, optimisation algorithms, segmentation. Biodiversity research studies the “variation of species level such as population, biocenosis life at all levels of biological organisation” (Gaston ecosystems, and biospheres require controlled and Spicer 2004) and is used to refer to “the whole experiments of their environment. For example, range of activities traditionally connected with one may wish to project the impacts of future inventorying and studying living resources” increase in yearly mean temperature on an (Lévêque and Mounolou 2003). Such studies ecosystem, or to attempt to computationally frequently encounter large datasets of biological introduce an invasive species to see its effects on a entities, especially in functional interactions population. In other cases, one may wish to between different levels of organisms and their increase the biological time and processes of a effects on the modifying and shaping of the virtual ecosystem in order to predict its outcome in environment. As research in biodiversity becomes a hundred years’ time. Other important needs are to complex, computer modelling and simulation visualise, not by charts and graphs but by utilising become a necessary means for conducting interactive computer graphics, the real-time experiments so that questions that cannot be interaction of a biological community in order to answered by traditional approaches may be observe the ecosystem dynamics. In any case, there resolved through the use of technology. The is much to be explored in the mergence of feasibility of computer modelling of nature is computers and biology for biodiversity research. amplified in studies in biocompexity, which is Traditional modelling in ecology depended upon defined by Lévêque and Mounolou as “the result of statistical and probabilistic procedures such as functional interactions between biological entities, classification and regression trees, generalised at all levels of organization, and their biological, linear models, multivariate adaptive regression chemical, physical and social environments” where splines, and artificial neural networks. According observational studies of complex entities above the to some comparative studies (Cairns 2001, Miller and Franklin 2002, Muñoz and Felicísimo 2004), such methods often produce inconsistent results. In correspondence e-mail: [email protected] 5 CH’NG. – AN EFFICIENT SEGMENTATION ALGORITHM FOR ENTITY INTERACTION the worst case, they are fraught with errors and applications of biology in our engineering inaccuracies as factors considered crucial are often endeavors.” not being taken into account. A number of critiques Most, if not all of Artificial Life-based modelling have recently questioned the validity of modelling techniques are Individual-Based Models (IBMs) strategies for predicting the natural distribution of and an extension of it called Evolutionary species. For example, a research review (Pearson Individual-Based Model (EIBM) (Bornhofen and and Dawson 2003) showed that many factors other Lattaud 2006), or Agent-based models. Breckling than climate determine species distributions and et al. stated that ‘IBMs contrasts with common the dynamics of distribution changes. While ecological models which frequently operate on the traditional approaches have been focused on the population level and represent a population as an identification of a species’ bioclimatic envelope, overall state, thereby specifying rules how the crucial factors such as biotic interactions (Connell overall state changes (Breckling et al. 2005). 1961, Silander and Antonovics 1982, Davis et al. IBMs was identified in a visionary article by 1998) and evolutionary change (Woodward 1990, Huston et al. (Huston et al. 1988) as a potential Davis and Shaw 2001, Thomas et al. 2001) are not model for simulating the effects of individual taken into account and therefore making variation, spatial processes, cumulative stress, and predictions erroneous and misleading. Species natural complexities that are difficult with classical dispersal is another factor that has been approaches: “individual-based models allow disregarded, Pearson and Dawson (Pearson and ecological modellers to investigate types of Dawson 2003) suggest that migration limitations questions that have been difficult or impossible to such as landscape barriers, deforestation, and man- address using the state-variable approach.” made habitats can obstruct species movement and Individual-Based Models have been thoroughly should be taken into account in such models. As reviewed up to 1999 (Grimm 1999) and 2005 incorporating these factors into the procedures of (Grimm and Railsback 2005) followed by a recent traditional approaches is difficult, it becomes review of EIBM (Bornhofen and Lattaud 2006). necessary to explore new approaches for ecological Another review of the approach have shown that modelling (Ch'ng 2009). individual-based models can also benefit from Nature has intrinsic problem-solving principles concepts in Complex Adaptive Systems (Railsback and nature-inspired design seems a potential area 2001). These novel modelling techniques could for discovering new methodology for research. potentially resolve issues outlined earlier, such as There may not be a better way to model biological biotic interaction, evolutionary change, and species life than to simply mimic its designs by using the dispersal barriers. What distinguishes IBMs from algorithmic format to simulate the various levels of other “individual-oriented” models that complexities (molecular, species, ecosystem, and acknowledge the individual level in some way but biosphere). This synthesis of nature formulates still adhere mainly to the classical modelling algorithms by extracting nature’s principle rules in paradigm? There are four criterion: (1) the degree order that biological organisms can be replicated to which the complexity of the individual's life within the confines of voltage and silicon. Indeed, cycle is reflected in the model; (2) whether or not the aim of the experimental science of Artificial the dynamics of resources used by individuals are Life (Langton 1995) attempts to understand the explicitly represented; (3)whether real or integer mechanisms behind living systems by synthesising numbers are used to represent the size of a them so that the structure and functions of these population; and (4) the extent to which variability systems may be understood and applied. One of its among individuals of the same age is considered founders (Langton 1990) states that “By extending (Uchmanski J and V. 1996, Grimm and Railsback the horizons of empirical research in biology 2005). Since the emergence phenomenon observed beyond the territory currently circumscribed by in IBMs reflects that of ecological systems, three life-as-we-know-it, the study of Artificial Life properties characterises them (Breckling et al. gives us access to the domain of life-as-it-could-be, 2005): (1) they do not exist on the level of isolated and it is within this vastly larger domain that we subsystems; (2) they emerge on higher levels as a must ground general theories of biology and in result of interactions of the subsystems; and (3) which we will discover novel and practical new properties appear at one level of a system and 6 CH’NG. – AN EFFICIENT SEGMENTATION ALGORITHM FOR ENTITY INTERACTION are not deducible from the observation of the lower graphics (Snyder and Barr 1987, Deussen et al. levels units or compartments of the system. This 1998, Soler et al. 2003) but algorithms have not requires the modelling of individuals rather than at been formalised for improving the efficiency of the population level. Such techniques require the large entity interactions. modelling of individual biological life, including Data structures for managing and categorising their genotype, phenotype and dynamics. This large collections of data are available. The simplest implies the requirements for large computing perhaps is the array data structure. More advanced resources as hundreds of thousands of calculations structures are hierarchical data structures, which is are performed for each individual as they react and based on
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