Robotic assembly line balancing (RALB) is specific for robotic assembly, where different robots require different assembly times to perform the same task because of their specialization. The problem includes optimal assignment of robots to line stations and balanced distribution of work between different stations. It aims at maximizing the production rate of the line. This problem is solved by ...

In this paper we review some previously published experimental results in which a simple hillclimbing algorithm-Random Mutation Hill-Climbing (RMHC)-significantly outperforms a genetic algorithm on a simple "Royal Road" function. vVe present an analysis of RMHC followed by an analysis of an "idealized" genetic algorithm (IGA) that is in turn significantly faster than RMHC. We isolate the featur...

This paper presents the application of an action module planning method to an experimental climbing robot named LIBRA. The method searches for a sequence of physically realizable actions, called action modules, to produce a plan for a given task. The search is performed with a hierarchical selection process that uses task and configuration filters to reduce the action module inventory to a reas...

Previous work investigating the performance of genetic algorithms (GAs) has attempted to develop a set of fitness landscapes, called “Royal Roads” functions, which should be ideally suited for search with GAs. Surprisingly, many studies have shown that genetic algorithms actually perform worse than random mutation hill-climbing on these landscapes, and several different explanations have been o...

Test incorporations are program transformations that improve the performance of generate-and-test procedures by moving information out of the \test" and into the \generator." The test information is said to be \incorporated" into the generator so that items produced by the generator are guaranteed to satisfy the incorporated test. This article proposes and investigates the hypothesis that a gen...

Hierarchical latent class (HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are not. This paper is concerned with the problem of learning HLC models from data. We apply the idea of structural EM to a hill-climbing algorithm for this task described in an accompanying paper (Zhang et al. 2003) and show empirically that the improved algorithm can...

A simple recurrent neural network is trained on a one-step look ahead prediction task for symbol sequences of the context-sensitive a n b n c n language. Using an evolutionary hill climbing strategy for incremental learning the network learns to predict sequences of strings up to depth n = 12. Experiments and the algorithms used are described. The activation of the hidden units of the trained n...

This paper introduces the Extremum Consistency (EC) algorithm for avoiding local maxima and minima in a specialised domain. The most notable difference between this approach and others in the literature is that it places a greater importance on the width or consistency of an extremum than on its height or depth (amplitude). Short-term, high amplitude extrema can be encountered in many typical s...

We present an information-theoretic analysis of Darwin’s theory of evolution, modeled as a hill-climbing algorithm on a fitness landscape. Our space of possible organisms consists of computer programs, which are subjected to random mutations. We study the random walk of increasing fitness made by a single mutating organism. In two different models we are able to show that evolution will occur a...

In this paper we describe an optimal reconfiguration planning algorithm that morphs a grounded truss structure of known geometry into a new geometry. The plan consists of a sequence of paths to move truss elements to their new locations that generate the new truss geometry. The trusses are grounded and remain connected at all time. Intuitively, the algorithm grows gradually the new truss struct...