Any neuro-evolutionary algorithm that solves complex problems needs to deal with the issue of computational complexity. We show how a neural network (feed-forward, recurrent or RBF) can be transformed and then compiled in order to achieve fast execution speeds without requiring dedicated hardware like FPGAs. The compiled network uses a simple external data structure-a vector-for its parameters. This allows the weights of the neural network to be optimised by the evolutionary process without the need to re-compile the structure. In an experimental comparison our method effects a speedup of factor 5-10 compared to the standard method of evaluation (i.e., traversing a data structure with optimised C++ code).

In this article a new method is presented to obtain a full and precise calibration of camera-robot systems with eye-in-hand cameras. It achieves a simultaneous and numerically stable calibration of intrinsic and extrinsic camera parameters by analysing the image coordinates of a single point marker placed in the environment of the robot. The method works by first determining a rough initial estimate of the camera pose in the tool coordinate frame. This estimate is then used to generate a set of uniformly distributed calibration poses from which the object is visible. The measurements obtained in these poses are then used to obtain the exact parameters with CMA-ES (Covariance Matrix Adaptation Evolution Strategy), a derandomised variant of an evolution strategy optimiser. Minimal claims on the surrounding area and flexible handling of environmental and kinematical limitations make this method applicable to a range of robot setups and camera models. The algorithm runs autonomously without supervision and does not need manual adjustments. Our problem formulation is directly in the 3D space which helps in minimising the resulting calibration errors in the robot's task space. Both simulations and experimental results with a real robot show a very good convergence and high repeatability of calibration results without requiring user-supplied initial estimates of the calibration parameters.

In this article we present a new method for the pruning of unnecessary connections from neural networks created by an evolutionary algorithm (neuro-evolution). Pruning not only decreases the complexity of the network but also improves the numerical stability of the parameter optimisation process. We show results from experiments where connection pruning is incorporated into EANT2, an evolutionary reinforcement learning algorithm for both the topology and parameters of neural networks. By analysing data from the evolutionary optimisation process that determines the network's parameters, candidate connections for removal are identified without the need for extensive additional calculations.

In this article we present results from experiments where a edge detector was learned from scratch by EANT2, a method for evolutionary reinforcement learning. The detector is constructed as a neural network that takes as input the pixel values from a given image region-the same way that standard edge detectors do. However, it does not have any per-image parameters. A comparison between the evolved neural networks and two standard algorithms, the Sobel and Canny edge detectors, shows very good results.

This article presents results from experiments where a detector for defects in visual inspection images was learned from scratch by EANT2, a method for evolutionary reinforcement learning. The detector is constructed as a neural network that takes as input statistical data on filter responses from a bank of image filters applied to an image region. Training is done on example images with weakly labelled defects. Experiments show good results of EANT2 in an application area where evolutionary methods are rare.

Assertions had their origin in program verification. For the systems developed in industry, construction of assertions and their use in showing program correctness is a near-impossible task. However, they can be used to show that some key properties are satisfied during program execution. We first present a survey of the special roles that assertions can play in object oriented software construction. We then analyse such assertions by relating them to the case study of an automatic surveillance system. In particular, we address the following two issues: What types of assertions can be used most effectively in the context of object oriented software? How can you discover them and where should they be placed? During maintenance, both the design and the software are continuously changed. These changes can mean that the original assertions, if present, are no longer valid for the new software. Can we automatically derive assertions for the changed software?.

Abstract Software processes vary across organizations and over time. Managing this process diversity is a delicate balancing act between creative, healthy diversity and chaos. In this paper, we examine a particular aspect of this issue, namely some relationships between diversity in software processes, software evolution and the quality of software products and processes. Our main contribution is to distinguish between two broad kinds of process diversity, which we call latitudinal and longitudinal process diversity. To illustrate the differences between these two, we examine the case of a medium‐sized system (50 000 lines of C++ code) which has undergone major changes during its lifetime of 10 years. The software was originally developed by an individual academic using a research‐oriented process to develop a standalone proof‐of‐concept system. In a current multi‐team project, involving three industrial and three academic partners, the software has been adapted for integration as a subsystem of a near‐market product. We suggest ways in which the observed process diversity seems to be linked to a change in the software's propensity for evolution, and we discuss the impact of this on both product and process quality. Copyright © 2003 John Wiley & Sons, Ltd.

Unregulated evolution of software often leads to software ageing which not only makes the product difficult to maintain but also breaks the consistency between design and implementation. In such a case, it may become necessary to re-engineer the software so that it becomes maintainable again. In this paper we present the case study of the reengineering of the People Tracking subsystem of a surveillance system written in C++. We discuss the problems, the challenges and the approaches taken, and we show how the re-engineered product is now better maintainable. We also discuss the generation of the relevant artefacts - from requirement document through to design document.