Emile H. L. Aarts

Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing

SIMULATED ANNEALING. Combinatorial Optimization. Simulated Annealing. Asymptotic Convergence. Finite-Time Approximation. Simulated Annealing in Practice. Parallel Simulated Annealing Algorithms. BOLTZMANN MACHINES. Neural Computing. Boltzmann Machines. Combinatorial Optimization and Boltzmann Machines. Classification and Boltzmann Machines. Learning and Boltzmann Machines. Appendix. Bibliography. Indices.

Genetic Local Search Algorithms for the Travelling Salesman Problem

We briefly review previous attempts to generate near-optimal solutions of the Traveling Salesman Problem by applying Genetic Algorithms. Following the lines of Johnson [1990] we discuss some possibilities for speeding up classical Local Search algorithms by casting them into a genetic frame. In an experimental study two such approaches, viz. Genetic Local Search with 2-Opt neighbourhoods and Lin-Kernighan neighbourhoods, respectively, are compared with the corresponding classical multi-start Local Search algorithms, as well as with Simulated Annealing and Threshold Accepting, using 2-Opt neighbourhoods. As to be expected a genetic organization of Local Search algorithms can considerably improve upon performance though the genetic components alone can hardly counterbalance a poor choice of the neighbourhoods.

Global Convergence of Genetic Algorithms: A Markov Chain Analysis

In this paper we are trying to make a step towards a concise theory of genetic algorithms (GAs) and simulated annealing (SA). First, we set up an abstract stochastic algorithm for treating combinatorial optimization problems. This algorithm generalizes and unifies genetic algorithms and simulated annealing, such that any GA or SA algorithm at hand is an instance of our abstract algorithm. Secondly, we define the evolution belonging to the abstract algorithm as a Markov chain and find conditions implying that the evolution finds an optimum with probability 1. The results obtained can be applied when designing the components of a genetic algorithm.

New research perspectives on Ambient Intelligence

Ten years of AmI research have led to many new insights and understandings about the way highly interactive environments should be designed to meet the requirement of being truly unobtrusive and supportive from an end-user perspective. Probably the most revealing finding is the fact that, in addition to cognitive intelligence and computing, also elements from social intelligence and design play a dominant role in the realization of the vision. In this paper we discuss these novel insights and their resulting impact on the AmI research landscape. We introduce a number of new AmI research perspectives that are related to social intelligence and in addition we argue that new ways of working are required applying the concept of Experience Research resulting in a true user-centered approach to Ambient Intelligence.

A quantitative analysis of the simulated annealing algorithm: A case study for the traveling salesman problem

A quantitative study is presented of the typical behavior of the simulated annealing algorithm based on a cooling schedule presented previously by the authors. The study is based on the analysis of numerical results obtained by systematically applying the algorithm to a 100-city traveling salesman problem. The expectation and the variance of the cost are analyzed as a function of the control parameter of the cooling schedule. A semiempirical average-case performance analysis is presented from which estimates are obtained on the expectation of the average final result obtained by the simulated annealing algorithm as a function of the distance parameter, which determines the decrement of the control parameter.

Ambient intelligence: visualizing the future

As technologies in the area of storage, connectivity and displays are rapidly evolving and business development is pointing to the direction of the experience economy, the vision of Ambient Intelligence is positioning the human needs central to technology development. Equipped with a special research instrument called HomeLab, scenarios of Ambient Intelligence are implemented and tested. As two examples of bringing real user experiences through display technology into the digital home, research on creating the feeling of immersion and the feeling of being connected, are discussed. Results from this work indicate that visual displays can indeed be used beyond simple information rendering but can actually play an important role in creating user experiences.

Boltzmann machines for travelling salesman problems

Boltzmann machines are proposed as a massively parallel alternative to the (sequential) simulated annealing algorithm. Our approach is tailored to the travelling salesman problem, but it can also be applied to a more general class of combinatorial optimization problems. For two distinct 0–1 programming formulations of the travelling salesman problem (as a linear and as a quadratic assignment problem) it is shown that near-optimal solutions can be obtained by mapping the corresponding 0–1 variables onto the logic computing elements of a Boltzmann machine, and by transforming the cost functions corresponding to the 0–1 programming formulations into the consensus function associated with the Boltzmann machine. Results of computer simulations are presented for two problem instances, i.e. with 10 cities and 30 cities, respectively.

Personalizing persuasive technologies

This paper discusses how persuasive technologies can be made adaptive to users. We present persuasion profiling as a method to personalize the persuasive messages used by a system to influence its users. This type of personalization can be based on explicit measures of users? tendencies to comply to distinct persuasive strategies: measures based on standardized questionnaire scores of users. However, persuasion profiling can also be implemented using implicit, behavioral measures of user traits. We present three case studies involving the design, implementation, and field deployment of personalized persuasive technologies, and we detail four design requirements. In each case study we show how these design requirements are implemented. In the discussion we highlight avenues for future research in the field of adaptive persuasive technologies. Author-HighlightsPersuasive technologies can be more effective if they are personalized.We introduce persuasion profiles to personalize persuasive messages.Persuasion profiles can be effective using implicit or explicit measures.In three case studies we show the effects of personalized persuasion.

Persuasion in ambient intelligence

Although the field of persuasive technologies has lately attracted a lot of attention, only recently the notion of ambient persuasive technologies was introduced. Ambient persuasive technologies can be integrated into every aspect of life, and as such have greater persuasive power than the traditional box like machines. This article discusses ambient persuasion and poses a model that structures the knowledge from social sciences on persuasion, attitude change, and behavior change. Using this model the challenges of ambient persuasive technologies to fulfill its persuasive promises are identified. From the ambient persuasion model it is clear that ambient persuasive technologies can go beyond traditional persuasive technologies by being context and situational aware, by interpreting individual differences between users, and by being a social actor in their own right.

Combinatorial optimization on a Boltzmann machine

We discuss the problem of solving (approximately) combinatorial optimization problems on a Boltzmann machine. It is shown for a number of combinatorial optimization problems how they can be mapped directly onto a Boltzmann machine by choosing appropriate connection patterns and connection strengths. In this way maximizing the consensus in the Boltzmann machine is equivalent to finding an optimal solution of the corresponding optimization problem. The approach is illustrated by numerical results obtained by applying the model of Boltzmann machines to randomly generated instances of the independent set, the max cut, and the graph coloring problem. For these instances the Boltzmann machine finds near-optimal solutions whose quality is comparable to that obtained with sequential simulated annealing algorithms. The advantage of the Boltzmann machine is the potential for carrying out operations in parallel. For the problems we have been investigating, this results in a considerable speedup over the sequential simulated annealing algorithms.