Coevolutive adaptation of fitness landscape for solving the testing problem

IEEE International Conference on Systems, Man, and Cybernetics. Nashville, TN, 8-11 October 2000A general framework, called Uniform Coevolution, is introduced to overcome the testing problem in evolutionary computation methods. This framework is based on competitive evolution ideas where the solution and example sets are evolving by means of a competition to generate difficult test beds for the solutions in a gradual way. The method has been tested with two different problems: the robot navigation problem and the density parity problem in cellular automata. In both test cases using evolutive methods, the examples used in the learning process biased the solutions found. The main characteristics of the Uniform Coevolution method are that it smoothes the fitness landscape and, that it obtains “ideal learner examples”. Results using uniform coevolution show a high value of generality, compared with non co-evolutive approaches

Distance modulation competitive co-evolution method to find initial configuration independent cellular automata rules

IEEE International Conference on Systems, Man, and Cybernetics. Tokyo, 12-15 October 1999.One of the main problems in machine learning methods based on examples is the over-adaptation. This problem supposes the exact adaptation to the training examples losing the capability of generalization. A solution of these problems arises in using large sets of examples. In most of the problems, to achieve generalized solutions, almost infinity examples sets are needed. This make the method useless in practice. In this paper, one way to overcome this problem is proposed, based on biological competitive evolution ideas. The evolution is produced as a result of a competition between sets of solutions and sets of examples, trying to beat each other. This mechanism allows the generation of generalized solutions using short example sets

Distributed Multi-Robot Learning using Particle Swarm Optimization

This thesis studies the automatic design and optimization of high-performing robust controllers for mobile robots using exclusively on-board resources. Due to the often large parameter space and noisy performance metrics, this constitutes an expensive optimization problem. Population-based learning techniques have been proven to be effective in dealing with noise and are thus promising tools to approach this problem. We focus this research on the Particle Swarm Optimization (PSO) algorithm, which, in addition to dealing with noise, allows a distributed implementation, speeding up the optimization process and adding robustness to failure of individual agents. In this thesis, we systematically analyze the different variables that affect the learning process for a multi-robot obstacle avoidance benchmark. These variables include algorithmic parameters, controller architecture, and learning and testing environments. The analysis is performed on experimental setups of increasing evaluation time and complexity: numerical benchmark functions, high-fidelity simulations, and experiments with real robots. Based on this analysis, we apply the distributed PSO framework to learn a more complex, collaborative task: flocking. This attempt to learn a collaborative task in a distributed manner on a large parameter space is, to our knowledge, the first of such kind. In addition, we address the problem of noisy performance evaluations encountered in these robotic tasks and present a %new distributed PSO algorithm for dealing with noise suitable for resource-constrained mobile robots due to its low requirements in terms of memory and limited local communication

The emergence of self-organisation in social systems: the case of the geographic industrial clusters

The objective of this work is to use complexity theory to propose a new interpretation of industrial clusters. Industrial clusters constitute a specific type of econosphere, whose driving principles are self-organisation, economies of diversity and a configuration that optimises the exploration of diversity starting from the configuration of connectivity of the system. This work shows the centrality of diversity by linking complexity theory (intended as "a method for understanding diversity"') to different concepts such as power law distributions, self-organisation, autocatalytic cycles and connectivity.I propose a method to distinguish self-organising from non self-organising agglomerations, based on the correlation between self-organising dynamics and power law network theories. Self-organised criticality, rank-size rule and scale-free networks theories become three aspects indicating a common underlying pattern, i.e. the edge of chaos dynamic. I propose a general model of development of industrial clusters, based on the mutual interaction between social and economic autocatalytic cycle. Starting from Kauffman's idea(^2) on the autocatalytic properties of diversity, I illustrate how the loops of the economies of diversity are based on the expansion of systemic diversity (product of diversity and connectivity). My thesis provides a way to measure systemic diversity. In particular I introduce the distinction between modular innovation at the agent level and architectural innovation at the network level and show that the cluster constitutes an appropriate organisational form to manage the tension and dynamics of simultaneous modular and architectural innovation. The thesis is structured around two propositions: 1. Self-organising systems are closer to a power law than hierarchical systems or aggregates (collection of parts). For industrial agglomerations (SLLs), the closeness to a power law is related to the degree of self-organisation present in the agglomeration, and emerges in the agglomeration’s structural and/or behavioural properties subject to self-organising dynamic.2. Self-organising systems maximise the product of diversity times connectivity at a rate higher than hierarchical systems

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

Hybrid Genetic Relational Search for Inductive Learning

An important characteristic of all natural systems is the ability to acquire knowledge through experience and to adapt to new situations. Learning is the single unifying theme of all natural systems. One of the basic ways of gaining knowledge is through examples of some concepts.For instance, we may learn how to distinguish a dog from other creatures after that we have seen a number of creatures, and after that someone (a teacher, or supervisor) told us which creatures are dogs and which are not. This way of learning is called supervised learning. Inductive Concept Learning (ICL) constitutes a central topic in machine learning. The problem can be formulated in the following manner: given a description language used to express possible hypotheses, a background knowledge, a set of positive examples, and a set of negative examples, one has to find a hypothesis which covers all positive examples and none of the negative ones. This is a supervised way of learning, since a supervisor has already classified the examples of the concept into positive and negative examples. The so learned concept can be used to classify previously unseen examples. In general deriving general conclusions from specific observation is called induction. Thus in ICL, concepts are induced because obtained from the observation of a limited set of training examples. The process can be seen as a search process. Starting from an initial hypothesis, what is done is searching the space of the possible hypotheses for one that fits the given set of examples. A representation language has to be chosen in order to represent concepts, examples and the background knowledge. This is an important choice, because this may limit the kind of concept we can learn. With a representation language that has a low expressive power we may not be able to represent some problem domain, because too complex for the language adopted. On the other side, a too expressive language may give us the possibility to represent all problem domains. However this solution may also give us too much freedom, in the sense that we can build concepts in too many different ways, and this could lead to the impossibility of finding the right concept. We are interested in learning concepts expressed in a fragment of first--order logic (FOL). This subject is known as Inductive Logic Programming (ILP), where the knowledge to be learn is expressed by Horn clauses, which are used in programming languages based on logic programming like Prolog. Learning systems that use a representation based on first--order logic have been successfully applied to relevant real life problems, e.g., learning a specific property related to carcinogenicity. Learning first--order hypotheses is a hard task, due to the huge search space one has to deal with. The approach used by the majority of ILP systems tries to overcome this problem by using specific search strategies, like the top-down and the inverse resolution mechanism. However, the greedy selection strategies adopted for reducing the computational effort, render techniques based on this approach often incapable of escaping from local optima. An alternative approach is offered by genetic algorithms (GAs). GAs have proved to be successful in solving comparatively hard optimization problems, as well as problems like ICL. GAs represents a good approach when the problems to solve are characterized by a high number of variables, when there is interaction among variables, when there are mixed types of variables, e.g., numerical and nominal, and when the search space presents many local optima. Moreover it is easy to hybridize GAs with other techniques that are known to be good for solving some classes of problems. Another appealing feature of GAs is represented by their intrinsic parallelism, and their use of exploration operators, which give them the possibility of escaping from local optima. However this latter characteristic of GAs is also responsible for their rather poor performance on learning tasks which are easy to tackle by algorithms that use specific search strategies. These observations suggest that the two approaches above described, i.e., standard ILP strategies and GAs, are applicable to partly complementary classes of learning problems. More important, they indicate that a system incorporating features from both approaches could profit from the different benefits of the approaches. This motivates the aim of this thesis, which is to develop a system based on GAs for ILP that incorporates search strategies used in successful ILP systems. Our approach is inspired by memetic algorithms, a population based search method for combinatorial optimization problems. In evolutionary computation memetic algorithms are GAs in which individuals can be refined during their lifetime.Eiben, A.E. [Promotor]Marchiori, E. [Copromotor

Unintended Side Effects of the Digital Transition: European Scientists’ Messages from a Proposition-Based Expert Round Table

We present the main messages of a European Expert Round Table (ERT) on the unintended side effects (unseens) of the digital transition. Seventeen experts provided 42 propositions from ten different perspectives as input for the ERT. A full-day ERT deliberated communalities and relationships among these unseens and provided suggestions on (i) what the major unseens are; (ii) how rebound effects of digital transitioning may become the subject of overarching research; and (iii) what unseens should become subjects of transdisciplinary theory and practice processes for developing socially robust orientations. With respect to the latter, the experts suggested that the “ownership, economic value, use and access of data” and, related to this, algorithmic decision-making call for transdisciplinary processes that may provide guidelines for key stakeholder groups on how the responsible use of digital data can be developed. A cluster-based content analysis of the propositions, the discussion and inputs of the ERT, and a theoretical analysis of major changes to levels of human systems and the human–environment relationship resulted in the following greater picture: The digital transition calls for redefining economy, labor, democracy, and humanity. Artificial Intelligence (AI)-based machines may take over major domains of human labor, reorganize supply chains, induce platform economics, and reshape the participation of economic actors in the value chain. (Digital) Knowledge and data supplement capital, labor, and natural resources as major economic variables. Digital data and technologies lead to a post-fuel industry (post-) capitalism. Traditional democratic processes can be (intentionally or unintentionally) altered by digital technologies. The unseens in this field call for special attention, research and management. Related to the conditions of ontogenetic and phylogenetic development (humanity), the ubiquitous, global, increasingly AI-shaped interlinkage of almost every human personal, social, and economic activity and the exposure to indirect, digital, artificial, fragmented, electronically mediated data affect behavioral, cognitive, psycho-neuro-endocrinological processes on the level of the individual and thus social relations (of groups and families) and culture, and thereby, the essential quality and character of the human being (i.e., humanity). The findings suggest a need for a new field of research, i.e., focusing on sustainable digital societies and environments, in which the identification, analysis, and management of vulnerabilities and unseens emerging in the sociotechnical digital transition play an important role

De-Sign Environment Landscape City Atti

La VI Conferenza Internazionale sul Disegno, De_Sign Environment Landscape City_Genova 2020 tratta di: Rilievo e Rappresentazione dell’Architettura e dell’Ambiente; Il Disegno per il paesaggio; Disegni per il Progetto: tracce - visioni e pre-visioni; I margini i segni della memoria e la città in progress; Cultura visiva e comunicazione dall’idea al progetto; Le emergenze architettoniche; Il colore e l’ambiente; Percezione e identità territoriale; Patrimonio iconografico culturale paesaggistico: arte, letteratura e ricadute progettuali; Segni e Disegni per il Design e Rappresentazione avanzata. Federico Babina, architetto e graphic designer presenta ARCHIVISION, e Eduardo Carazo Lefort, Docente dell’Università di Valladolid e Targa d’Oro dell’Unione Italiana Disegno la Lectio Magistralis. The VI International Conference on Drawing, De_Sign Environment Landscape City_Genoa 2020, deals with: Survey and Representation of Architecture and the Environment; Drawing for the landscape; De-signs for the Project: traces-visions and previews; Margins, signs of memory and the city in progress; Visual culture and communication from idea to project; Architectural emergencies; The color and the environment; Perception and territorial identity; Landscape cultural iconographic heritage: art, literature and design implications; Signs and Drawings for Design and Advanced Representation. Federico Babina, architect and graphic designer presents ARCHIVISION, and Professor Eduardo Carazo Lefort-University of Valladolid and Gold Plate of the Italian Design Union presents his Lectio Magistralis