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.
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.
12974717
A System for Learning Routes and Schedules with Genetic Algorithms
5345095
A Parallel Genetic Heuristic for the Quadratic Assignment Problem
195710721
Special Issue: Machine Learning in Design
This issue of AIEDAM is based on a workshop on Machine Learning in Design held at the 1996 Conference on Artificial Intelligence in Design, AID'96 (Gero & Sudweeks, 1996), the third of such workshops, with the previous two being held at AID'92 (Gero, 1992) and at AID'94 (Gero & Sudweeks, 1994). The first two workshops also resulted in special issues of AIEDAM (Maher et al., 1994; Duffy et al., 1996).
34188181
Literacy and Numeracy Edutainment Packages for Disaffected Young Learners
The UK Government has conducted research into skills gaps within the UK population. It has concluded that there is an alarmingly large proportion of the population with low literacy and numeracy skills.
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].
18346977
Expectation-based Learning in Design
Design problems typically have a very large number of problem states, many of which cannot be anticipated at the onset of the design. Some design problem states are characterized by as many as hundreds of parameters. Given these amounts of uncertainty and information, AI design systems faced with learning tasks cannot know from the beginning what needs to be learned, and whether these needs will remain the same. In this abstract we describe how LEAD (Learning Expectations in Agent-based Design), a multiagent system for parametric and configuration design, addresses these challenges in design learning.
34635988
Virtual Travel Training for People with Learning Disabilities Accessing Employment Including the Introduction to the Special Thematic Session "Virtual Reality"
The purpose of this project is to develop a Virtual Learning Environment to assist in the travel training of a group of people with learning disabilities to enable them to gain access to training and employment opportunities. To achieve this a realistic Virtual Travel in Sutton Package will be developed to assist people with special needs to learn independent travel skills, and to assess the effectiveness of the software in achieving this aim.