5345095
A Parallel Genetic Heuristic for the Quadratic Assignment Problem
12974717
A System for Learning Routes and Schedules with Genetic Algorithms
44788901
Parallel genetic algorithms with local search
This paper presents methods of applying local search to global optimization problems. The most common approach, multistart, selects the best solution from restarts of local search from random starting points. Partitional methods augment local search with general principles concerning the location of global optima in real space, significantly improving the effectiveness of local search in function optimization problems. Standard partitional methods, however, are not directly applicable to combinatorial optimization problems. We describe a genetic algorithm, GALO, that is similar to the partitional methods, but can be applied to combinatorial problems. Empirical results are presented for a parallel implementation of GALO that show it to be effective for the quadratic assignment problem.
1332842
A functional framework for the implementation of genetic algorithms: Comparing Haskell and Standard ML
We present our experience of developing a generic functiona l framework for the implementation of genetic algorithms (GAs). We have implemented two versions of the framework, one in Haskell and the other in Standard ML. This paper describes the structure of the framework, and com pares the performance of the two versions using the Royal Road Problem in g enetic algorithms. The Haskell version is found to be vastly slower than the equi valent Standard ML version, and the reasons for this have not been exposed by p rofiling the code. The maximum heap size and the proportion of time spent on garb a e collection is also greater in the Haskell version. In some cases, garbag e collection accounts for almost 50% of the total execution time.
10497047
Markov Random Field Modelling of Royal Road Genetic Algorithms
Markov Random Fields (MRFs) [5] are a class of probabalistic models that have been applied for many years to the analysis of visual patterns or textures. In this paper, our objective is to establish MRFs as an interesting approach to modelling genetic algorithms. Our approach bears strong similarities to recent work on the Bayesian Optimisation Algorithm [9], but there are also some significant differences. We establish a theoretical result that every genetic algorithm problem can be characterised in terms of a MRF model. This allows us to construct an explicit probabilistic model of the GA fitness function. The model can be used to generate chromosomes, and derive a MRF fitness measure for the population. We then use a specific MRF model to analyse two Royal Road problems, relating our analysis to that of Mitchell et al. [7].
8669815
Guiding Agent Learning in Design
In this paper we discuss the need for learning in multi-agent design systems, and the variety of forms it might take. We propose a particular method of guiding learning in these systems, describe an architecture for its implementation, and discuss how the learning should be evaluated.
37811985
Evaluating Design Knowledge Compilation Mechanisms
28079143
Provably Secure Implicit Certificate Schemes
Optimal mail certificates, introduced in [12], are efficient types of implicit certificates which offer many advantages over traditional (explicit) certificates. For example, an optimal mail certificate is small enough to fit on a two-dimensional digital postal mark together with a digital signature. This paper defines a general notion of security for implicit certificates, and proves that optimal mail certificates are secure under this definition.