Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Inductive Pattern Formation

With the extended computational limits of algorithmic recursion, scientific investigation is transitioning away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points. This investigation addressed this issue of quantitative synthesis. Local space was developed by the Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces. In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat

GPU Computing for Cognitive Robotics

This thesis presents the first investigation of the impact of GPU computing on cognitive robotics by providing a series of novel experiments in the area of action and language acquisition in humanoid robots and computer vision. Cognitive robotics is concerned with endowing robots with high-level cognitive capabilities to enable the achievement of complex goals in complex environments. Reaching the ultimate goal of developing cognitive robots will require tremendous amounts of computational power, which was until recently provided mostly by standard CPU processors. CPU cores are optimised for serial code execution at the expense of parallel execution, which renders them relatively inefficient when it comes to high-performance computing applications. The ever-increasing market demand for high-performance, real-time 3D graphics has evolved the GPU into a highly parallel, multithreaded, many-core processor extraordinary computational power and very high memory bandwidth. These vast computational resources of modern GPUs can now be used by the most of the cognitive robotics models as they tend to be inherently parallel. Various interesting and insightful cognitive models were developed and addressed important scientific questions concerning action-language acquisition and computer vision. While they have provided us with important scientific insights, their complexity and application has not improved much over the last years. The experimental tasks as well as the scale of these models are often minimised to avoid excessive training times that grow exponentially with the number of neurons and the training data. This impedes further progress and development of complex neurocontrollers that would be able to take the cognitive robotics research a step closer to reaching the ultimate goal of creating intelligent machines. This thesis presents several cases where the application of the GPU computing on cognitive robotics algorithms resulted in the development of large-scale neurocontrollers of previously unseen complexity enabling the conducting of the novel experiments described herein.European Commission Seventh Framework Programm

Inter-sector partnerships: complex dynamics and patterns of behaviour

This thesis examines inter-sector partnership processes from a complex dynamical systems perspective. Inter-sector partnerships is increasingly researched both as new forms of government and policy making, and in the sustainability field. The theories traditionally used to analyse this topic fail to confront their dynamics as a whole. Recent approaches draw on complexity theory but, by pre-defining the principles for analysis, constrain the complete understanding of these phenomena. This thesis combines an inductive and deductive approach to explore the complex principles that drive agents’ interactions both at an emergent level (macro), process level (meso) and a causality level (micro). This aims at 1) providing a theoretical and methodological framework to study inter-sector partnerships as complex dynamical processes; and 2) advancing the understanding of social dynamics in the field of complexity theory. This work is based on two case studies collected during fieldwork in Brazil and Ecuador using participatory inquiry and semi-structured interviews to account for the multiple agents, perspectives and components of these processes. These experiences reflect dissimilar topics of collaboration and context conditions intended to provide various scenarios of work and highlight regularities through cross-examination. The results show that, despite the differences, a common pattern of behaviour governs the creation and evolution of multi-stakeholder processes in both case studies. This pattern shows five stages driven by different complex principles: 1) the creation of niche opportunities; 2) the occupation of this niche by a new agent; 3) the emergence of collective behaviour and inter-sector partnerships; 4) the influence of the collaborative process in the system dynamic; and 5) the expansion of a new dynamic in the system. The results provide new insights into the functioning of complex social systems and show that multi-stakeholder processes represent (1) a phase transition in the system dynamics; and 2) a poised state in the system dynamic at the complex regime or edge of chaos, state where the system optimises its capacity to adapt to change, innovate and perform complex tasks. These findings have a direct practical implication by providing practitioners and policy makers with a tool (qualitative dynamical modelling) to promote or reinforce inter-sector partnerships, and to drive social systems to this intermediate regime of optimal performance, the edge of chaos

Open Source Software Evolution and Its Dynamics

This thesis undertakes an empirical study of software evolution by analyzing open source software (OSS) systems. The main purpose is to aid in understanding OSS evolution. The work centers on collecting large quantities of structural data cost-effectively and analyzing such data to understand software evolution dynamics (the mechanisms and causes of change or growth). We propose a multipurpose systematic approach to extracting program facts (e. g. , function calls). This approach is supported by a suite of C and C++ program extractors, which cover different steps in the program build process and handle both source and binary code. We present several heuristics to link facts extracted from individual files into a combined system model of reasonable accuracy. We extract historical sequences of system models to aid software evolution analysis. We propose that software evolution can be viewed as Punctuated Equilibrium (i. e. , long periods of small changes interrupted occasionally by large avalanche changes). We develop two approaches to study such dynamical behavior. One approach uses the evolution spectrograph to visualize file level changes to the implemented system structure. The other approach relies on automated software clustering techniques to recover system design changes. We discuss lessons learned from using these approaches. We present a new perspective on software evolution dynamics. From this perspective, an evolving software system responds to external events (e. g. , new functional requirements) according to Self-Organized Criticality (SOC). The SOC dynamics is characterized by the following: (1) the probability distribution of change sizes is a power law; and (2) the time series of change exhibits long range correlations with power law behavior. We present empirical evidence that SOC occurs in open source software systems

Coevolution in Complex Networks : An analysis of socio-natural interactions for wetlands management

Coevolution between natural and social systems comprises interaction, reciprocal dynamics and reciprocal adaptation. The notion derives primarily from evolutionary biology, but also from the study of complex systems. This dissertation aims to: “develop the means to assess the effects of different human interventions on the future coevolution of interacting natural and social systems.” The method that I develop is termed ‘topological network analysis’, highlighting my focus on the topology – number and pattern of interactions – of complex networks. A socio-natural network integrates interactions within and between a natural and a social system. Topological network analysis simulates and compares the effect of different human interventions on the network’s topology. It comprises four steps: 1. construction of a reference socio-natural network capturing the current situation for a given region; 2. specification of alternative development paths for the region; 3. translation of these paths into change in the network; and 4. comparison of the alternative paths according to their estimate impacts on the robustness of the network and so the stability of the system . This last step leads to management insights. Topological network analysis is illustrated by considering conversion of a stand of mangroves in the Philippines. The dissertation focuses on human intervention into ecosystem and on the potential for subsequent biodiversity loss. Topological network may be best applied to decision problems or management issues involving differential effects on species’ survival.Nijkamp, P. [Promotor]Opschoor, J.B. [Promotor

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u