Open-ended Coevolution and the Emergence of Complex Irreducible Functional Units in Iterated Number Sequence Games

Inden B. Open-ended Coevolution and the Emergence of Complex Irreducible Functional Units in Iterated Number Sequence Games. In: Proceedings of the 14th annual conference on genetic and evolutionary computation. New York, NY, USA: ACM; 2012: 113-200

Evolving neural networks to follow trajectories of arbitrary complexity

Many experiments have been performed that use evolutionary algorithms for learning the topology and connection weights of a neural network that controls a robot or virtual agent. These experiments are not only performed to better understand basic biological principles, but also with the hope that with further progress of the methods, they will become competitive for automatically creating robot behaviors of interest. However, current methods are limited with respect to the (Kolmogorov) complexity of evolved behavior. Using the evolution of robot trajectories as an example, we show that by adding four features, namely (1) freezing of previously evolved structure, (2) temporal scaffolding, (3) a homogeneous transfer function for output nodes, and (4) mutations that create new pathways to outputs, to standard methods for the evolution of neural networks, we can achieve an approximately linear growth of the complexity of behavior over thousands of generations. Overall, evolved complexity is up to two orders of magnitude over that achieved by standard methods in the experiments reported here, with the major limiting factor for further growth being the available run time. Thus, the set of methods proposed here promises to be a useful addition to various current neuroevolution methods

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

Complexity: Theoretical and methodological applications for sociology

This thesis examines the usefulness of Complexity as a new tool for sociology. Complexity, as its own branch of study, developed from the new science of Chaos. Recent paradigmatic disputes occurring in the scientific community have been the force of a growing sense of change in the way many different disciplines view complex systems. Since it is evident that social systems are typically highly complex, it makes sense that a scientific paradigm, which investigates the nature of complex systems, should also be applicable to social systems. Science now argues that the old Newtonian clockwork mentalities and classical experimental models cannot adequately describe highly complex systems. Instead anti-reductionist and nonlinear theories and methods may be much better suited for the task. The sociological relevance of Complexity---both its conceptual framework and its methodologies---is important and timely as we reach the limits of our current knowledge using standard reductionist thinking and methods

Altruistically Inclined?: The Behavioral Sciences, Evolutionary Theory, and the Origins of Reciprocity

Altruistically Inclined? examines the implications of recent research in the natural sciences for two important social scientific approaches to individual behavior: the economic/rational choice approach and the sociological/anthropological. It considers jointly two controversial and related ideas: the operation of group selection within early human evolutionary processes and the likelihood of modularity—domain-specific adaptations in our cognitive mechanisms and behavioral predispositions. Experimental research shows that people will often cooperate in one-shot prisoner\u27s dilemma (PD) games and reject positive offers in ultimatum games, contradicting commonly accepted notions of rationality. Upon first appearance, predispositions to behave in this fashion could not have been favored by natural selection operating only at the level of the individual organism. Emphasizing universal and variable features of human culture, developing research on how the brain functions, and refinements of thinking about levels of selection in evolutionary processes, Alexander J. Field argues that humans are born with the rudiments of a PD solution module—and differentially prepared to learn norms supportive of it. His emphasis on failure to harm, as opposed to the provision of affirmative assistance, as the empirically dominant form of altruistic behavior is also novel. The point of departure and principal point of reference is economics. But Altruistically Inclined? will interest a broad range of scholars in the social and behavioral sciences, natural scientists concerned with the implications of research and debates within their fields for the conduct of work elsewhere, and educated lay readers curious about essential features of human nature.https://scholarcommons.scu.edu/faculty_books/1325/thumbnail.jp