The Emergence of Diversity and Stability: from Biological Systems to Machine Learning

The observation of emergent properties of biological systems has been the inspiration of successful technologies opening new fields of computer science like artificial neural nets, swarm intelligence algorithms, evolutive algorithms, etc. In this work we focus on the emergence of negative feedback cycles: self-regulatory mechanisms able to react to alterations of some environmental parameters (temperature, gas concentrations, solar light, etc.) in order to compensate, preserving the environment in a state suitable for life. We make the hypothesis that speciation events play a central role for feedback formation and, and in order to select the negative cycles, the arising species need to be strongly connected to the environment, therefore the speciation needs to be sympatric (a speciation mode where new species arise without geographical isolation). As an intermediate result, we propose a simulative model of sympatric speciation and apply it to the field of evolutive algorithms. We propose some variations of the standard island model, a model used in evolutive algorithms to evolve multiple populations, to obtain dynamics similar to the sympatric speciation model, enhancing the diversity and the stability of the evolutive system. Then we propose a technique to define a metric and calculate approximated distances on very complex genetic spaces (a recurring problem for several evolutionary algorithms approaches). Finally, we describe the more complex model of negative feedback cycles emergence and discuss the problems that, in the current model formulation, make it not applicable to real world problems but only to ad hoc defined resource spaces; conclusively we propose possible solutions and some applications

Parallel Genetic Algorithm based Thresholding Schemes for Image Segmentation

In this thesis, the problem of image segmentation has been addressed using the notion of thresholding.Since the focus of this work is primarily on object/objects background classification and fault detection in a given scene, the segmentation problem is viewed as a classification problem. In this regard, the notion of thresholding has been used to classify the range of gray values and hence classifies the image. The gray level distributions of the original image or the proposed feature image have been used to obtain the optimal threshold. Initially, PGA based class models have been developed to classify different classes of a nonlinear multimodal function. This problem is formulated where the nonlinear multimodal function is viewed as consisting of multiple class distributions.Each class could be represented by the niche or peaks of that class.Hence, the problem has been formulated to detect the peaks of the functions. PGA based clustering algorithm has been proposed to maintain stable sub-populations in the niches and hence the peaks could be detected. A new interconnection model has been proposed for PGA to accelerate the rate of convergence to the optimal solution. Convergence analysis of the proposed PGA based algorithm has been carried out and is shown to converge to the solution. The proposed PGA based clustering algorithm could successfully be tested for different classes and is found to converge much faster than that of GA based clustering algorithm