Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms

Nature-inspired optimization-based clustering techniques are powerful, robust and more sophisticated than the conventional clustering methods due to their stochastic and heuristic characteristics. Unfortunately, these algorithms suffer with several drawbacks such as the tendency to be trapped or stagnate into local optima and slow convergence rates. The latter drawbacks are consequences of the difficulty in balancing the exploration and exploitation processes which directly affect the final quality of the clustering solutions. Hence, this research has proposed three enhanced frameworks, namely, Optimized Gravitational-based (OGC), Density-Based Particle Swarm Optimization (DPSO), and Variance-based Differential Evolution with an Optional Crossover (VDEO) frameworks for data clustering. In the OGC framework, the exhibited explorative search behavior of the Gravitational Clustering (GC) algorithm has been addressed by (i) eliminating the agent velocity accumulation, and (ii) integrating an initialization method of agents using variance and median to subrogate the exploration process. Moreover, the balance between the exploration and exploitation processes in the DPSO framework is considered using a combination of (i) a kernel density estimation technique associated with new bandwidth estimation method and (ii) estimated multi-dimensional gravitational learning coefficients. Lastly, (i) a single-based solution representation, (ii) a switchable mutation scheme, (iii) a vector-based estimation of the mutation factor, and (iv) an optional crossover strategy are proposed in the VDEO framework. The overall performances of the three proposed frameworks have been compared with several current state-of-the-art clustering algorithms on 15 benchmark datasets from the UCI repository. The experimental results are also thoroughly evaluated and verified via non-parametric statistical analysis. Based on the obtained experimental results, the OGC, DPSO, and VDEO frameworks achieved an average enhancement up to 24.36%, 9.38%, and 11.98% of classification accuracy, respectively. All the frameworks also achieved the first rank by the Friedman aligned-ranks (FA) test in all evaluation metrics. Moreover, the three frameworks provided convergent performances in terms of the repeatability. Meanwhile, the OGC framework obtained a significant performance in terms of the classification accuracy, where the VDEO framework presented a significant performance in terms of cluster compactness. On the other hand, the DPSO framework favored the balanced state by producing very competitive results compared to the OGC and DPSO in both evaluation metrics. As a conclusion, balancing the search behavior notably enhanced the overall performance of the three proposed frameworks and made each of them an excellent tool for data clustering

Journal of Telecommunications and Information Technology, 2011, nr 4

kwartalni

Investigating the Evolutionary Dynamics of Drug Resistance in Colorectal Cancer

PhD ThesesCancer resistance evolution was presumed to result from either a pre-existing or acquired mutation that survives treatment, re-populating the tumour following therapy. However, it appears cancer cells can adopt both genetic and non-genetic mechanisms to evade treatment, and a much broader range of evolutionary scenarios could drive resistance evolution. Here, I first develop models that explicitly capture both genetic and non-genetic sources of phenotypic variation in cell populations evolving resistance to therapy. I show that, given different parameters controlling the change in a resistance phenotype per division and the relative fitness cost of resistance, I can distinguish between various evolutionary scenarios, including those that lead to the same proportion of resistance. I subsequently combine these theoretical models with a long-term drug-treatment experiment in vitro: I employ a high-resolution lineage tracing technique and metronomic chemotherapy exposure in two colorectal cancer cell models. In one cell-line - HCT116 - the lineage distributions are consistent with a resistance phenotype being held at a low frequency by a high reversion phenotypic switching rate, or a high relative fitness cost. The other cell-line – SW620 – exhibits a response that is consistent with a broad range of evolutionary scenarios, all of which have relatively lower switching rates and fitness costs, whilst maintaining the resistant phenotype at a higher frequency within the population. My data show a role for either plasticity or a high fitness cost in the evolution of drug resistance in these colorectal cancer cell models. These results highlight the importance of including the diverse evolutionary scenarios that produce phenotypic differences within the population when modelling cancer cells' response to therapy. As stymieing resistance requires hampering a tumour's evolution, I argue that designing more effective treatment strategies will depend on accurately describing these diverse routes to resistance

Macroevolution: Explanation, Interpretation and Evidence

info:eu-repo/semantics/publishedVersio

Non-spurious correlations between genetic and linguistic diversities in the context of human evolution

This thesis concerns human diversity, arguing that it represents not just some form of noise, which must be filtered out in order to reach a deeper explanatory level, but the engine of human and language evolution, metaphorically put, the best gift Nature has made to us. This diversity must be understood in the context of (and must shape) human evolution, of which the Recent Out-of-Africa with Replacement model (ROA) is currently regarded, especially outside palaeoanthropology, as a true theory. It is argued, using data from palaeoanthropology, human population genetics, ancient DNA studies and primatology, that this model must be, at least, amended, and most probably, rejected, and its alternatives must be based on the concept of reticulation. The relationships between the genetic and linguistic diversities is complex, including interindividual genetic and behavioural differences (behaviour genetics) and inter-population differences due to common demographic, geographic and historic factors (spurious correlations), used to study (pre)historical processes. It is proposed that there also exist nonspurious correlations between genetic and linguistic diversities, due to genetic variants which can bias the process of language change, so that the probabilities of alternative linguistic states are altered. The particular hypothesis (formulated with Prof. D. R. Ladd) of a causal relationship between two human genes and one linguistic typological feature is supported by the statistical analysis of a vast database of 983 genetic variants and 26 linguistic features in 49 Old World populations, controlling for geography and known linguistic history. The general theory of non-spurious correlations between genetic and linguistic diversities is developed and its consequences and predictions analyzed. It will very probably profoundly impact our understanding of human diversity and will offer a firm footing for theories of language evolution and change. More specifically, through such a mechanism, gradual, accretionary models of language evolution are a natural consequence of post-ROA human evolutionary models. The unravellings of causal effects of inter-population genetic differences on linguistic states, mediated by complex processes of cultural evolution (biased iterated learning), will represent a major advance in our understanding of the relationship between cultural and genetic diversities, and will allow a better appreciation of this most fundamental and supremely valuable characteristic of humanity - its intrinsic diversity

Complexity, Language, and Life: Mathematical Approaches

In May 1984 the Swedish Council for Scientific Research convened a small group of investigators at the scientific research station at Abisko, Sweden, for the purpose of examining various conceptual and mathematical views of the evolution of complex systems. The stated theme of the meeting was deliberately kept vague, with only the purpose of discussing alternative mathematically based approaches to the modeling of evolving processes being given as a guideline to the participants. In order to limit the scope to some degree, it was decided to emphasize living rather than nonliving processes and to invite participants from a range of disciplinary specialities spanning the spectrum from pure and applied mathematics to geography and analytic philosophy. The results of the meeting were quite extraordinary; while there was no intent to focus the papers and discussion into predefined channels, an immediate self-organizing effect took place and the deliberations quickly oriented themselves into three main streams: conceptual and formal structures for characterizing system complexity; evolutionary processes in biology and ecology; the emergence of complexity through evolution in natural languages. The chapters presented in this volume are not the proceedings of the meeting. Following the meeting, the organizers felt that the ideas and spirit of the gathering should be preserved in some written form, so the participants were each requested to produce a chapter, explicating the views they presented at Abisko, written specifically for this volume. The results of this exercise are contained in this book

A quantitative approach to the study of syntactic evolution

The dissertation covers the experimentation of quantitative algorithmic procedures for the study of language evolution. In particular, the inquiry is based on the application of quantitative methods originally designed within molecular biology and population genetics to a parametric comparative dataset: The goal is to infer hypotheses regarding genealogical relationships between a specific set of languages, accounting for the role of areal convergence in linguistic variation, and to evaluate them in light of the traditional accounts provided by historical linguistics. The first focus is on the comparison between language evolution and biological evolution. The idea is that some important features of language development may also be identified drawing a parallel with the biological domain. On the whole, this analysis seems to show that language evolution and biological evolution are considerably different in some respects, but that the dissimilarities do not prevent the application of quantitative reconstruction procedures. Then most recent generative views on syntactic change are taken into consideration, showing that they are perfectly compatible with the evolutionary account outlined. To this end, basic notions regarding the cognitive-biolinguistic and the formal aspects of generative grammar are illustrated and, once the parametric perspective on synchronic language variation is clarified, the discussion is dedicated to the extension of the parametric approach to the explanation of diachronic phenomena, including genealogical development and contact. The successive step is the presentation of diverse methods of comparison adopted in historical linguistics and population genetics and, in particular, of the “Parametric comparison method”: The parallel between the latter and the procedures of investigation used in molecular biology paves the way to the introduction of the relevant quantitative techniques of phylogenetic reconstruction. After having outlined the overview of the principal datasets used so far to perform quantitative investigations on the history of languages, the parametric dataset is presented and overview of “traditional” and quantitative-based proposals regarding the genealogical classification of the languages included in the investigation is provided. The last section of the work covers the illustration of the quantitative analyses carried out. The preliminary character-based and distance-based review of the dataset is followed by the discussion on the choice of the phylogenetic methods adopted. Then the first outfit of phylogenies reconstructions on the full dataset is offered and commented on in detail. The successive focus is on possible strategies to account for homoplasy (i.e. chance and borrowing): An empirically-based selection of parameters and suggestions regarding the way in which parameters might be weighted according to their genealogical relevance are proposed. Finally, some tentative analyses concerning the possibility of detecting and accounting for borrowing in phylogenetic trees, the reconstruction of ancestral states and the mapping of syntactic distances onto the diachronic and the diatopic dimensions of variation are introduced. On the whole, the quantitative analyses appear to provide good indications of diverse facts: That phylogenetic techniques are to a large extent effectively applicable to the study of syntactic evolution, that the parametric comparison may successfully help shedding light on both short- and long-range genealogical relationships, and that traces of proper genealogical relatedness are likely to be preserved (and to be recoverable despite homoplasy) at the level of “macro-comparison”, like that instantiated in the parametric data