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].
2693848
A parallel heuristic for quadratic assignment problems
Abstract The quadratic assignment problem represents an important class of problems with applications as diverse as facility layout and data analysis. The importance of these applications coupled with the fact that the quadratic assignment problem is NP-hard has encouraged the development of heuristics because optimal seeking procedures have been restricted to very small versions of the problem. This paper describes a new parallel heuristic, SAGA, for the quadratic assignment problem. SAGA is a cascaded hybrid of a genetic algorithm and simulated annealing. In addition to details regarding SAGA and its implementation, this paper also describes the performance of SAGA on two standard problems taken from the literature. The results from these problems show SAGA to be superior to the most commonly employed heuristic in solution quality, and for large problems it is also superior in solution time.
15121856
Agent-based and analytical modeling to evaluate the effectiveness of greenbelts
We present several models of residential development at the rural–urban fringe to evaluate the effectiveness of a greenbelt located beside a developed area, for delaying development outside the greenbelt. First, we develop a mathematical model, under two assumptions about the distributions of service centers, that represents the trade-off between greenbelt placement and width, their effects on the rate of development beyond the greenbelt, and how these interact with spatial patterns of aesthetic quality and the locations of services. Next, we present three agent-based models (ABMs) that include agents with the potential for heterogeneous preferences and a landscape with the potential for heterogeneous attributes. Results from experiments run with a one-dimensional ABM agree with the starkest of the results from the mathematical model, strengthening the support for both models. Further, we present two different two-dimensional ABMs and conduct a series of experiments to supplement our mathematical analysis. These include examining the effects of heterogeneous agent preferences, multiple landscape patterns, incomplete or imperfect information available to agents, and a positive aesthetic quality impact of the greenbelt on neighboring locations. These results suggest how width and location of the greenbelt could help determine the effectiveness of greenbelts for slowing sprawl, but that these relationships are sensitive to the patterns of landscape aesthetic quality and assumptions about service center locations. # 2004 Elsevier Ltd. All rights reserved.
8868489
Design Simplification by Analogical Reasoning
This paper presents our research on design simplification by analogical reasoning. It defines a “simplification problem”, then describes our approach to solving design simplification problems by analogical reasoning. One of the important contributions of the research is the use of relevance for guiding the analogical reasoning process. Its benefits are analyzed using a set of experiments. Finally, the paper draws conclusions and proposes directions for future research
2947153
Dimensions of machine learning in design
Many of the design systems developed in recent years incorporate some machine learning. The number of such systems already available, and the multitude of design learning opportunities that are slowly being revealed, suggest that the time is ripe to attempt to put these developments into a systematic framework. Consequently, in this paper we present a set of dimensions for machine learning in design research. We hope that it can be used as a guide for comparing existing work, and that it may suggest new directions for future exploration in this area.
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.