Bidirectional incremental evolution in extrinsic evolvable hardware

Evolvable Hardware (EHW) has been proposed as a new technique to design complex systems. Often, complex systems turn out to be very difficult to evolve. The problem is that a general strategy is too difficult for the evolution process to discover directly. This paper proposes a new approach that performs incremental evolution in two directions: from complex system to sub-systems and from sub-systems back to complex system. In this approach, incremental evolution gradually decomposes a complex problem into some sub-tasks. In a second step, we gradually make the tasks more challenging and general. Our approach automatically discovers the sub-tasks, their sequence as well as circuit layout dimensions. Our method is tested in a digital circuit domain and compared to direct evolution. We show that our bidirectional incremental approach can handle more complex, harder tasks and evolve them more effectively, then direct evolution

A general learning co-evolution method to generalize autonomous robot navigation behavior

Congress on Evolutionary Computation. La Jolla, CA, 16-19 July 2000.A new coevolutive method, called Uniform Coevolution, is introduced, to learn weights for a neural network controller in autonomous robots. An evolutionary strategy is used to learn high-performance reactive behavior for navigation and collision avoidance. The coevolutive method allows the evolution of the environment, to learn a general behavior able to solve the problem in different environments. Using a traditional evolutionary strategy method without coevolution, the learning process obtains a specialized behavior. All the behaviors obtained, with or without coevolution have been tested in a set of environments and the capability for generalization has been shown for each learned behavior. A simulator based on the mini-robot Khepera has been used to learn each behavior. The results show that Uniform Coevolution obtains better generalized solutions to example-based problems

Incremental Consistency Checking in Delta-oriented UML-Models for Automation Systems

Automation systems exist in many variants and may evolve over time in order to deal with different environment contexts or to fulfill changing customer requirements. This induces an increased complexity during design-time as well as tedious maintenance efforts. We already proposed a multi-perspective modeling approach to improve the development of such systems. It operates on different levels of abstraction by using well-known UML-models with activity, composite structure and state chart models. Each perspective was enriched with delta modeling to manage variability and evolution. As an extension, we now focus on the development of an efficient consistency checking method at several levels to ensure valid variants of the automation system. Consistency checking must be provided for each perspective in isolation, in-between the perspectives as well as after the application of a delta.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857

Universal distribution of Lyapunov exponents for products of Ginibre matrices

Starting from exact analytical results on singular values and complex eigenvalues of products of independent Gaussian complex random $N\times N$ matrices also called Ginibre ensemble we rederive the Lyapunov exponents for an infinite product. We show that for a large number $t$ of product matrices the distribution of each Lyapunov exponent is normal and compute its $t$-dependent variance as well as corrections in a $1/t$ expansion. Originally Lyapunov exponents are defined for singular values of the product matrix that represents a linear time evolution. Surprisingly a similar construction for the moduli of the complex eigenvalues yields the very same exponents and normal distributions to leading order. We discuss a general mechanism for $2\times 2$ matrices why the singular values and the radii of complex eigenvalues collapse onto the same value in the large-$t$ limit. Thereby we rederive Newman's triangular law which has a simple interpretation as the radial density of complex eigenvalues in the circular law and study the commutativity of the two limits $t\to\infty$ and $N\to\infty$ on the global and the local scale. As a mathematical byproduct we show that a particular asymptotic expansion of a Meijer G-function with large index leads to a Gaussian.Comment: 36 pages, 6 figure

Evolving Neural Networks for the Capture Game

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Complexity, strategic thinking and organisational change

Comparative considerations of strategy from complexity paradigm and Newtonian paradigm perspectives are discussed in the light of three ideological dispositions towards the future. We term them defensive, opportunist, and goal oriented. Over the years, the strategy literature has identified a number of strategic archetypes (e.g. Miller and Freisen, 1978). What is interesting from our point of view is the patterns of reasoning that underpin them. The study of ideology has identified qualitative patterns of reasoning which underpin different types of strategic decision in both the fields of politics and strategic management. This paper considers three patterns of reasoning and considers how they relate to the complexity and Newtonian paradigms

Evolution of Neural Networks for Helicopter Control: Why Modularity Matters

The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so